Methods and systems for analysis of organ transplantation

ABSTRACT

Disclosed herein are methods of detecting, predicting or monitoring a status or outcome of a transplant in a transplant recipient.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/481,167, filed Sep. 9, 2014, which claims the benefit of U.S.Provisional Application No. 61/875,276 filed on Sep. 9, 2013, U.S.Provisional Application No. 61/965,040 filed on Jan. 16, 2014, U.S.Provisional Application No. 62/001,889 filed on May 22, 2014, U.S.Provisional Application No. 62/029,038 filed on Jul. 25, 2014, U.S.Provisional Application No. 62/001,909 filed on May 22, 2014, and U.S.Provisional Application No. 62/001,902 filed on May 22, 2014, all ofwhich are incorporated herein by reference in their entireties.

GOVERNMENT RIGHTS

The invention described herein was made with government support underGrant Numbers U19 A152349, U01 A1084146, and AI063603 awarded by theNational Institutes of Health. The United States Government has certainrights in the invention.

BACKGROUND

The current method for detecting organ rejection in a patient is abiopsy of the transplanted organ. However, organ biopsy results can beinaccurate, particularly if the area biopsied is not representative ofthe health of the organ as a whole (e.g., as a result of samplingerror). There can be significant differences between individualobservors when they read the same biopsies independently and thesediscrepancies are particularly an issue for complex histologies that canbe challenging for clinicians. Biopsies, especially surgical biopsies,can also be costly and pose significant risks to a patient. In addition,the early detection of rejection of a transplant organ may requireserial monitoring by obtaining multiple biopsies, thereby multiplyingthe risks to the patients, as well as the associated costs.

Transplant rejection is a marker of ineffective immunosuppression andultimately if it cannot be resolved, a failure of the chosen therapy.The fact that 50% of kidney transplant patients will lose their graftsby ten years post transplant reveals the difficulty of maintainingadequate and effective longterm immunosuppression. There is a need todevelop a minimally invasive, objective metric for detecting,identifying and tracking transplant rejection. In particular, there is aneed to develop a minimally invasive metric for detecting, identifyingand tracking transplant rejection in the setting of a confoundingdiagnosis, such as acute dysfunction with no rejection. This isespecially true for identifying the rejection of a transplanted kidney.For example, elevated creatinine levels in a kidney transplant recipientmay indicate either that the patient is undergoing an acute rejection oracute dysfunction without rejection. A minimally-invasive test that iscapable of distinguishing between these two conditions would thereforebe extremely valuable and would diminish or eliminate the need forcostly, invasive biopsies.

SUMMARY

The methods and systems disclosed herein may be used for detecting orpredicting a condition of a transplant recipient (e.g., acute transplantrejection, acute dysfunction without rejection, subclinical acuterejection, hepatitis C virus recurrence, etc.). In some aspects, amethod for detecting or predicting a condition of a transplant recipientcomprises a) obtaining a sample, wherein the sample comprises one ormore gene expression products from the transplant recipient; b)performing an assay to determine an expression level of the one or moregene expression products from the transplant recipient; and c) detectingor predicting the condition of the transplant recipient by applying analgorithm to the expression level determined in step (b), wherein thealgorithm is a classifier capable of distinguishing between at least twoconditions that are not normal conditions, and wherein one of the atleast two conditions is transplant rejection or transplant dysfunction.In another embodiment, a method for detecting or predicting a conditionof a transplant recipient comprises a) obtaining a sample, wherein thesample comprises one or more gene expression products from thetransplant recipient; b) performing an assay to determine an expressionlevel of the one or more gene expression products from the transplantrecipient; and c) detecting or predicting the condition of thetransplant recipient by applying an algorithm to the expression leveldetermined in step (b), wherein the algorithm is a classifier capable ofdistinguishing between at least two conditions that are not normalconditions, and wherein one of the at least two conditions is transplantrejection. In another embodiment, a method for detecting or predicting acondition of a transplant recipient comprises a) obtaining a sample,wherein the sample comprises one or more gene expression products fromthe transplant recipient; b) performing an assay to determine anexpression level of the one or more gene expression products from thetransplant recipient; and c) detecting or predicting the condition ofthe transplant recipient by applying an algorithm to the expressionlevel determined in step (b), wherein the algorithm is a classifiercapable of distinguishing between at least two conditions that are notnormal conditions, and wherein one of the at least two conditions istransplant dysfunction. In some cases, the transplant recipient is akidney transplant recipient. In some cases, the transplant recipient isa liver transplant recipient.

In some embodiments, a method of detecting or predicting a condition ofa transplant recipient comprises: a) obtaining a sample, wherein thesample comprises one or more gene expression products from thetransplant recipient; b) performing an assay to determine an expressionlevel of the one or more gene expression products from the transplantrecipient; and c) detecting or predicting the condition of thetransplant recipient by applying an algorithm to the expression leveldetermined in step (b), wherein the algorithm is capable ofdistinguishing between acute rejection and transplant dysfunction withno rejection. In some cases, the transplant dysfunction with norejection is acute transplant dysfunction with no rejection. In somecases, the transplant recipient is a kidney transplant recipient. Insome cases, the transplant recipient is a liver transplant recipient.

In an embodiment, a method of detecting or predicting a condition of atransplant recipient comprises: a) obtaining a sample, wherein thesample comprises five or more gene expression products from thetransplant recipient; b) an assay to determine an expression level ofthe five or more gene expression products from the transplant recipient,wherein the five or more gene expression products correspond to five ormore genes listed in Table 1a, 1b, 1c, or 1d, or any combinationthereof; and c) detecting or predicting the condition of the transplantrecipient based on the expression level determined in step (b). Inanother embodiment, a method of detecting or predicting a condition of atransplant recipient comprises: a) obtaining a sample, wherein thesample comprises five or more gene expression products from thetransplant recipient; b) an assay to determine an expression level ofthe five or more gene expression products from the transplant recipient,wherein the five or more gene expression products correspond to five ormore genes listed in Table 1a; and c) detecting or predicting thecondition of the transplant recipient based on the expression leveldetermined in step (b). In another embodiment, a method of detecting orpredicting a condition of a transplant recipient comprises: a) obtaininga sample, wherein the sample comprises five or more gene expressionproducts from the transplant recipient; b) an assay to determine anexpression level of the five or more gene expression products from thetransplant recipient, wherein the five or more gene expression productscorrespond to five or more genes listed in Table 1a, 1b, 1c, or 1d, inany combination.; and c) detecting or predicting the condition of thetransplant recipient based on the expression level determined in step(b). In another embodiment, a method of detecting or predicting acondition of a transplant recipient comprises: a) obtaining a sample,wherein the sample comprises five or more gene expression products fromthe transplant recipient; b) an assay to determine an expression levelof the five or more gene expression products from the transplantrecipient, wherein the five or more gene expression products correspondto five or more genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b,14b, 16b, 17b, or 18b, in any combination.; and c) detecting orpredicting the condition of the transplant recipient based on theexpression level determined in step (b). In some cases, the transplantrecipient is a kidney transplant recipient.

In an embodiment, a method of detecting or predicting a condition of atransplant recipient comprises: a) obtaining a sample, wherein thesample comprises one or more gene expression products from thetransplant recipient; b) performing an assay to determine an expressionlevel of the one or more gene expression products from the transplantrecipient; and c) detecting or predicting the condition of thetransplant recipient by applying an algorithm to the expression leveldetermined in step (b), wherein the algorithm is a three-way classifiercapable of distinguishing between at least three conditions, and whereinone of the at least three conditions is transplant rejection. In someembodiments, one of the at least three conditions is normal transplantfunction. In some embodiments, one of the at least three conditions istransplant dysfunction. In some embodiments, the transplant dysfunctionis transplant dysfunction with no rejection. In some cases, thetransplant dysfunction with no rejection is acute transplant dysfunctionwith no rejection. In another embodiment, the method disclosed hereinfurther comprises providing or terminating a treatment for thetransplant recipient based on the detected or predicted condition of thetransplant recipient.

In another aspect, a method of diagnosing, predicting or monitoring astatus or outcome of a transplant in a transplant recipient comprises:a) determining a level of expression of one or more genes in a samplefrom a transplant recipient, wherein the level of expression isdetermined by RNA sequencing; and b) diagnosing, predicting ormonitoring a status or outcome of a transplant in the transplantrecipient.

In another aspect, a method disclosed herein comprises the steps of: a)determining a level of expression of one or more genes in a sample froma transplant recipient; b) normalizing the expression level data fromstep (a) using a frozen robust multichip average (fRMA) algorithm toproduce normalized expression level data; c) producing one or moreclassifiers based on the normalized expression level data from step (b);and d) diagnosing, predicting or monitoring a status or outcome of atransplant in the transplant recipient based on the one or moreclassifiers from step (c). In another aspect, a method disclosed hereincomprises the steps of: a) determining a level of expression of aplurality of genes in a sample from a transplant recipient; and b)classifying the sample by applying an algorithm to the expression leveldata from step (a), wherein the algorithm is validated by a combinedanalysis of a sample with an unknown phenotype and a subset of a cohortwith known phenotypes.

In another aspect, the methods disclosed herein have an error rate ofless than about 40%. In some embodiments, the method has an error rateof less than about 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 2%, or 1%.For example, the method has an error rate of less than about 10%. Insome embodiments, the methods disclosed herein have an accuracy of atleast about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. For example,the method has an accuracy of at least about 70%. In some embodiments,the methods disclosed herein have a sensitivity of at least about 60%,65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. For example, the method has asensitivity of at least about 80%. In some embodiments, the methodsdisclosed herein have a positive predictive value of at least about 60%,65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. In some embodiments, themethods disclosed herein have a negative predictive value of at leastabout 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%.

In some embodiments, the gene expression products described herein areRNA (e.g., mRNA). In some embodiments, the gene expression products arepolypeptides. In some embodiments, the gene expression products are DNAcomplements of RNA expression products from the transplant recipient.

In an embodiment, the algorithm described herein is a trained algorithm.In another embodiment, the trained algorithm is trained with geneexpression data from biological samples from at least three differentcohorts. In another embodiment, the trained algorithm comprises a linearclassifier. In another embodiment, the linear classifier comprises oneor more linear discriminant analysis, Fisher's linear discriminant,Naïve Bayes classifier, Logistic regression, Perceptron, Support vectormachine (SVM) or a combination thereof. In another embodiment, thealgorithm comprises a Diagonal Linear Discriminant Analysis (DLDA)algorithm. In another embodiment, the algorithm comprises a NearestCentroid algorithm. In another embodiment, the algorithm comprises aRandom Forest algorithm or statistical bootstrapping. In anotherembodiment, the algorithm comprises a Prediction Analysis of Microarrays(PAM) algorithm. In another embodiment, the algorithm is not validatedby a cohort-based analysis of an entire cohort. In another embodiment,the algorithm is validated by a combined analysis with an unknownphenotype and a subset of a cohort with known phenotypes.

In another aspect, the one or more gene expression products comprisesfive or more gene expression products with different sequences. Inanother embodiment, the five or more gene expression products correspondto 200 genes or less. In another embodiment, the five or more geneexpression products correspond to less than (or at most) 200 geneslisted in Table 1c. In another embodiment, the five or more geneexpression products correspond to less than (or at most) 200 geneslisted in Table 1a. In another embodiment, the five or more geneexpression products correspond to less than (or at most) 200 geneslisted in Table 1a, 1b, 1c, or 1d, in any combination. In anotherembodiment, the five or more gene expression products correspond to lessthan about 200 genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b,14b, 16b, 17b, or 18b, in any combination. In another embodiment, thefive or more gene expression products correspond to less than or equalto 500 genes, to less than or equal to 400 genes, to less than or equalto 300 genes, to less than or equal to 250 genes, to less than or equalto 200 genes, to less than or equal to 150 genes, to less than or equalto 100 genes, to less than or equal to genes, to less than or equal to80 genes, to less than or equal to 50 genes, to less than or equal to 40genes, to less than or equal to genes, to less than or equal to 25genes, to less than or equal to 20 genes, at most 15 genes, or to lessthan or equal to 10 genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b,12b, 14b, 16b, 17b, or 18b, in any combination.

In one aspect, the biological samples are differentially classifiedbased on one or more clinical features. For example, the one or moreclinical features comprise status or outcome of a transplanted organ.

In another aspect, a three-way classifier is generated, in part, bycomparing two or more gene expression profiles from two or more controlsamples. In another embodiment, the two or more control samples aredifferentially classified as acute rejection, acute dysfunction norejection, or normal transplant function. In another embodiment, the twoor more gene expression profiles from the two or more control samplesare normalized. In another embodiment, the two or more gene expressionprofiles are not normalized by quantile normalization. In anotherembodiment, the two or more gene expression profiles from the two ormore control samples are normalized by frozen multichip average (fRMA).In another embodiment, the three-way classifier is generated by creatingmultiple computational permutations and cross validations using acontrol sample set. In some cases, a four-way classifier is used insteador in addition to a three-way classifier.

In another aspect, the sample is a blood sample or is derived from ablood sample. In another embodiment, the blood sample is a peripheralblood sample. In another embodiment, the blood sample is a whole bloodsample. In another embodiment, the sample does not comprise tissue froma biopsy of a transplanted organ of the transplant recipient. In anotherembodiment, the sample is not derived from tissue from a biopsy of atransplanted organ of the transplant recipient.

In another aspect, the assay is a microarray, SAGE, blotting, RT-PCR,sequencing and/or quantitative PCR assay. In another embodiment, theassay is a microarray assay. In another embodiment, the microarray assaycomprises the use of an Affymetrix Human Genome U133 Plus 2.0 GeneChip.In another embodiment, the mircroarray uses the Hu133 Plus 2.0 cartridgearrays plates. In another embodiment, the microarray uses the HTHG-U133+ PM array plates. In another embodiment, determining the assayis a sequencing assay. In another embodiment, the assay is a RNAsequencing assay. In another embodiment, the gene expression productscorrespond to five or more genes listed in Table 1c. In anotherembodiment, the gene expression products correspond to five or moregenes listed in Table 1a. In another embodiment, the gene expressionproducts correspond to five or more genes listed in Table 1a, 1b, 1c, or1d, in any combination. In another embodiment, the gene expressionproducts correspond to five or more genes listed in Table 1a, 1b, 1c,1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

In some embodiments, the transplant recipient has a serum creatininelevel of at least 0.4 mg/dL, 0.6 mg/dL, 0.8 mg/dL, 1.0 mg/dL, 1.2 mg/dL,1.4 mg/dL, 1.6 mg/dL, 1.8 mg/dL, 2.0 mg/dL, 2.2 mg/dL, 2.4 mg/dL, 2.6mg/dL, 2.8 mg/dL, 3.0 mg/dL, 3.2 mg/dL, 3.4 mg/dL, 3.6 mg/dL, 3.8 mg/dL,or 4.0 mg/dL. For example, the transplant recipient has a serumcreatinine level of at least 1.5 mg/dL. In another example, thetransplant recipient has a serum creatinine level of at least 3 mg/dL.

In another aspect, the transplant recipient is a recipient of an organor tissue. In some embodiments, the organ is an eye, lung, kidney,heart, liver, pancreas, intestines, or a combination thereof. In someembodiments, the transplant recipient is a recipient of tissue or cellscomprising: stem cells, induced pluripotent stem cells, embryonic stemcells, amnion, skin, bone, blood, marrow, blood stem cells, platelets,umbilical cord blood, cornea, middle ear, heart valve, vein, cartilage,tendon, ligament, or a combination thereof. In preferred embodiments ofany method described herein, the transplant recipient is a kidneytransplant recipient. In other embodiments, the transplant recipient isa liver recipient.

In another aspect, this disclosure provides classifier probe sets foruse in classifying a sample from a transplant recipient, wherein theclassifier probe sets are specifically selected based on aclassification system comprising two or more classes. In anotherembodiment, a classifier probe set for use in classifying a sample froma transplant recipient, wherein the classifier probe set is specificallyselected based on a classification system comprising three or moreclasses. In another embodiment, at least two of the classes are selectedfrom transplant rejection, transplant dysfunction with no rejection andnormal transplant function. In another embodiment, three of the three ormore classes are transplant rejection, transplant dysfunction with norejection and normal transplant function. In some cases, the transplantdysfunction with no rejection is acute transplant dysfunction with norejection.

In another aspect, a non-transitory computer-readable storage mediadisclosed herein comprises: a) a database, in a computer memory, of oneor more clinical features of two or more control samples, wherein i) thetwo or more control samples are from two or more transplant recipients;and ii) the two or more control samples are differentially classifiedbased on a classification system comprising three or more classes; b) afirst software module configured to compare the one or more clinicalfeatures of the two or more control samples; and c) a second softwaremodule configured to produce a classifier set based on the comparison ofthe one or more clinical features. In another embodiment, at least twoof the classes are selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. In anotherembodiment, all three classes are selected from transplant rejection,transplant dysfunction with no rejection and normal transplant function.

In another aspect, the storage media further comprising one or moreadditional software modules configured to classify a sample from atransplant recipient. In another embodiment, classifying the sample fromthe transplant recipient comprises a classification system comprisingthree or more classes. In another embodiment, at least two of theclasses are selected from transplant rejection, transplant dysfunctionwith no rejection and normal transplant function. In another embodiment,at least three of the classes are transplant rejection, transplantdysfunction with no rejection and normal transplant function.

In another aspect, a system comprising: a) a digital processing devicecomprising an operating system configured to perform executableinstructions and a memory device; b) a computer program includinginstructions executable by the digital processing device to classify asample from a transplant recipient comprising: i) a software moduleconfigured to receive a gene expression profile of one or more genesfrom the sample from the transplant recipient; ii) a software moduleconfigured to analyze the gene expression profile from the transplantrecipient; and iii) a software module configured to classify the samplefrom the transplant recipient based on a classification systemcomprising three or more classes. In another embodiment, at least one ofthe classes is selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. In anotherembodiment, at least two of the classes are selected from transplantrejection, transplant dysfunction with no rejection and normaltransplant function. In another embodiment, all three of the classes areselected from transplant rejection, transplant dysfunction with norejection and normal transplant function.

In another aspect, analyzing the gene expression profile from thetransplant recipient comprises applying an algorithm. In anotherembodiment, analyzing the gene expression profile comprises normalizingthe gene expression profile from the transplant recipient. In anotherembodiment, normalizing the gene expression profile does not comprisequantile normalization.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specificationare herein incorporated by reference in their entireties to the sameextent as if each individual publication or patent application wasspecifically and individually incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 shows a schematic overview of certain methods in the disclosure.

FIG. 2 shows a schematic overview of certain methods of acquiringsamples, analyzing results, transmitting reports over a computernetwork.

FIG. 3 shows a schematic of the workflows for cohort and bootstrappingstrategies for biomarker discovery and validation.

FIG. 4 shows a graph of the Area Under the Curve (AUCs) for the normaltransplant function (TX) versus acute rejection (AR), normal transplantfunction (TX) versus acute dysfunction no rejection (ADNR), and theacute rejection (AR) versus acute dysfunction no rejection (ADNR)comparisons for the locked nearest centroid (NC) classifier in thevalidation cohort.

FIG. 5 shows a graph of the Area Under the Curve (AUCs) for the normaltransplant function (TX) versus acute rejection (AR), normal transplantfunction (TX) versus acute dysfunction no rejection (ADNR), and theacute rejection (AR) versus acute dysfunction no rejection (ADNR) usingthe locked nearest centroid (NC) classifier on 30 blinded validationacute rejection (AR), acute dysfunction no rejection (ADNR) and normalfunction (TX) samples using the one-by-one strategy.

FIG. 6 shows a system for implementing the methods of the disclosure.

FIG. 7 shows a graph of AUCs for the 200-classifier set obtained fromthe full study sample set of 148 samples. These results demonstrate thatthere is no over-fitting of the classifier.

DETAILED DESCRIPTION OF THE INVENTION

Overview

The present disclosure provides novel methods for characterizing and/oranalyzing samples, and related kits, compositions and systems,particularly in a minimally invasive manner. Methods of classifying oneor more samples from one or more subjects are provided, as well asmethods of determining, predicting and/or monitoring an outcome orstatus of an organ transplant, and related kits, compositions andsystems. The methods, kits, compositions, and systems provided hereinare particularly useful for distinguishing between two or moreconditions or disorders associated with a transplanted organ or tissue.For example, they may be used to distinguish between acute transplantrejection (AR), acute dysfunction with no rejection (ADNR), and normallyfunctioning transplants (TX). Often, a three-way analysis or classifieris used in the methods provided herein.

This disclosure may be particularly useful for kidney transplantrecipients with elevated serum creatinine levels, since elevatedcreatinine may be indicative of AR or ADNR. The methods provided hereinmay inform the treatment of such patiecants and assist with medicaldecisions such as whether to continue or change immunosuppressivetherapies. In some cases, the methods provided herein may informdecisions as to whether to increase immunosuppression to treatimmune-mediated rejection if detected or to decrease immunosuppression(e.g., to protect the patient from unintended toxicities ofimmunosuppressive drugs when the testing demonstrates moreimmunosuppression is not required). The methods disclosed herein (e.g.,serial blood monitoring for rejection) may allow clinicians to make achange in an immunosuppression regimen (e.g., an increase, decrease orother modification in immunosuppression) and then follow the impact ofthe change on the blood profile for rejection as a function of time foreach individual patient through serial monitoring of a bodily fluid,such as by additional blood drawings.

An overview of certain methods in the disclosure is provided in FIG. 1.In some instances, a method comprises obtaining a sample from atransplant recipient in a minimally invasive manner (110), such as via ablood draw, urine capture, saliva sample, throat culture, etc. Thesample may comprise gene expression products (e.g., polypeptides, RNA,mRNA isolated from within cells or a cell-free source) associated withthe status of the transplant (e.g., AR, ADNR, normal transplantfunction, etc.). In some instances, the method may involvereverse-transcribing RNA within the sample to obtain cDNA that can beanalyzed using the methods described herein. The method may alsocomprise assaying the level of the gene expression products (or thecorresponding DNA) using methods such as microarray or sequencingtechnology (120). The method may also comprise applying an algorithm tothe assayed gene expression levels (130), wherein the algorithm iscapable of distinguishing signatures for two or more transplantationconditions (e.g., AR, ADNR, TX, SCAR, CAN/IFTA, etc.) such as two ormore non-normal transplant conditions (e.g., AR vs ADNR). Often, thealgorithm is a trained algorithm obtained by the methods providedherein. In some instances, the algorithm is a three-way classifier andis capable of performing multi-class classification of the sample (140).The method may further comprise detecting, diagnosing, predicting, ormonitoring the condition (e.g., AR, ADNR, TX, SCAR, CAN/IFTA etc.) ofthe transplant recipient. The methods may further comprise continuing,stopping or changing a therapeutic regimen based on the results of theassays described herein.

The methods, systems, kits and compositions provided herein may also beused to generate or validate an algorithm capable of distinguishingbetween at least two conditions of a transplant recipient (e.g., AR,ADNR, TX, SCAR, CAN/IFTA, etc.). The algorithm may be produced based ongene expression levels in various cohorts or sub-cohorts describedherein.

The methods, systems, kits and compositions provided herein may also becapable of generating and transmitting results through a computernetwork. As shown in FIG. 2, a sample (220) is first collected from asubject (e.g. transplant recipient, 210). The sample is assayed (230)and gene expression products are generated. A computer system (240) isused in analyzing the data and making classification of the sample. Theresult is capable of being transmitted to different types of end usersvia a computer network (250). In some instances, the subject (e.g.patient) may be able to access the result by using a standalone softwareand/or a web-based application on a local computer capable of accessingthe internet (260). In some instances, the result can be accessed via amobile application (270) provided to a mobile digital processing device(e.g. mobile phone, tablet, etc.). In some instances, the result may beaccessed by physicians and help them identify and track conditions oftheir patients (280). In some instances, the result may be used forother purposes (290) such as education and research.

Subjects

Often, the methods are used on a subject, preferably human, that is atransplant recipient. The methods may be used for detecting orpredicting a condition of the transplant recipient such as acuterejection (AR), acute dysfunction with no rejection (ADNR), chronicallograft nephropathy (CAN), interstitial fibrosis and tubular atrophy(IF/TA), subclinical rejection acute rejection (SCAR), hepatitis C virusrecurrence (HCV-R), etc. In some cases, the condition may be AR. In somecases, the condition may be ADNR. In some cases, the condition may beSCAR. In some cases, the condition may be transplant dysfunction. Insome cases, the condition may be transplant dysfunction with norejection. In some cases, the condition may be acute transplantdysfunction.

Typically, when the patient does not exhibit symptoms or test results oforgan dysfunction or rejection, the transplant is considered a normalfunctioning transplant (TX: Transplant eXcellent). An unhealthytransplant recipient may exhibit signs of organ dysfunction and/orrejection (e.g., an increasing serum creatinine). However, a subject(e.g., kidney transplant recipient) with subclinical rejection may havenormal and stable organ function (e.g. normal creatinine level andnormal eGFR). In these subjects, at the present time, rejection may bediagnosed histologically through a biopsy. A failure to recognize,diagnose and treat subclinical AR before significant tissue injury hasoccurred and the transplant shows clinical signs of dysfunction could bea major cause of irreversible organ damage. Moreover, a failure torecognize a chronic, subclinical immune-mediated organ damage and afailure to make appropriate changes in immunosuppressive therapy torestore a state of effective immunosuppression in that patient couldcontribute to late organ transplant failure. The methods disclosedherein can reduce or eliminate these and other problems associated withtransplant rejection or failure.

Acute rejection (AR) occurs when transplanted tissue is rejected by therecipient's immune system, which damages or destroys the transplantedtissue unless immunosuppression is achieved. T-cells, B-cells and otherimmune cells as well as possibly antibodies of the recipient may causethe graft cells to lyse or produce cytokines that recruit otherinflammatory cells, eventually causing necrosis of allograft tissue. Insome instances, AR may be diagnosed by a biopsy of the transplantedorgan. In the case of kidney transplant recipients, AR may be associatedwith an increase in serum creatinine levels. The treatment of AR mayinclude using immunosuppressive agents, corticosteroids, polyclonal andmonoclonal antibodies, engineered and naturally occurring biologicalmolecules, and antiproliferatives. AR more frequently occurs in thefirst three to 12 months after transplantation but there is a continuedrisk and incidence of AR for the first five years post transplant andwhenever a patient's immunosuppression becomes inadequate for any reasonfor the life of the transplant.

Acute dysfunction with no rejection (ADNR) is an abrupt decrease or lossof organ function without histological evidence of rejection from atransplant biopsy. Kidney transplant recipients with ADNR will oftenexhibit elevated creatinine levels. Unfortunately, the levels of kidneydysfunction based on serum creatinines are usually not significantlydifferent between AR and ADNR subjects.

Another condition that can be associated with a kidney transplant ischronic allograft nephropathy (CAN), which is characterized by a gradualdecline in kidney function and, typically, accompanied by high bloodpressure and hematuria. Histopathology of patients with CAN ischaracterized by interstitial fibrosis, tubular atrophy, fibroticintimal thickening of arteries and glomerulosclerosis typicallydescribed as IFTA. CAN/IFTA usually happens months to years after thetransplant though increased amounts of IFTA can be present early in thefirst year post transplant in patients that have received kidneys fromolder or diseased donors or when early severe ischemia perfusion injuryor other transplant injury occurs. CAN is a clinical phenotypecharacterized by a progressive decrease in organ transplant function. Incontrast, IFTA is a histological phenotype currently diagnosed by anorgan biopsy. In kidney transplants, interstitial fibrosis (IF) isusually considered to be present when the supporting connective tissuein the renal parenchyma exceeds 5% of the cortical area. Tubular atrophy(TA) refers to the presence of tubules with thick redundant basementmembranes, or a reduction of greater than 50% in tubular diametercompared to surrounding non-atrophic tubules. In certain instances,finding interstitial fibrosis and tubular atrophy (IFTA) on the biopsymay be early indicators that predict the later organ dysfunctionassociated with the clinical phenotype of CAN. Immunologically, CAN/IFTAusually represents a failure of effective longterm immunosuppression andmechanistically it is immune-mediated chronic rejection (CR) and caninvolve both cell and antibody-mediated mechanisms of tissue injury aswell as activation of complement and other blood coagulation mechanismsand can also involve inflammatory cytokine-mediated tissue activationand injury.

Subclinical rejection (SCAR) is generally a condition that ishistologically identified as acute rejection but without concurrentfunctional deterioration. For kidney transplant recipients, subclinicalrejection (SCAR) is histologically defined acute rejection that ischaracterized by tubulointerstitial mononuclear infiltration identifiedfrom a biopsy specimen, but without concurrent functional deterioration(variably defined as a serum creatinine not exceeding about 10%, 20% or25% of baseline values). A SCAR subject typically shows normal and/orstable serum creatinine levels. SCAR is usually diagnosed throughbiopsies that are taken at a fixed time after transplantation (e.g.protocol biopsies or serial monitoring biopsies) which are not driven byclinical indications but rather by standards of care. SCAR may besubclassified by some into acute SCAR (SCAR) or a milder form calledborderline SCAR (suspicious for acute rejection) based on the biopsyhistology.

A subject therefore may be a transplant recipient that has, or is atrisk of having a condition such as AR, ADNR, TX, CAN, IFTA, or SCAR. Insome instances, a normal serum creatinine level and/or a normalestimated glomerular filtration rate (eGFR) may indicate healthytransplant (TX) or subclinical rejection (SCAR). For example, typicalreference ranges for serum creatinine are 0.5 to 1.0 mg/dL for women and0.7 to 1.2 mg/dL for men, though typical kidney transplant patients havecreatinines in the 0.8 to 1.5 mg/dL range for women and 1.0 to 1.9 mg/dLrange for men. This may be due to the fact that most kidney transplantpatients have a single kidney. In some instances, the trend of serumcreatinine levels over time can be used to evaluate the recipient'sorgan function. This is why it may be important to consider both“normal” serum creatinine levels and “stable” serum creatinine levels inmaking clinical judgments, interpreting testing results, deciding to doa biopsy or making therapy change decisions including changingimmunosuppressive drugs. For example, the transplant recipient may showsigns of a transplant dysfunction or rejection as indicated by anelevated serum creatinine level and/or a decreased eGFR. In someinstances, a transplant subject with a particular transplant condition(e.g., AR, ADNR, CAN, etc.) may have an increase of a serum creatininelevel of at least 0.1 mg/dL, 0.2 mg/dL, 0.3 mg/dL, 0.4 mg/dL, 0.5 mg/dL,0.6 mg/dL, 0.7 mg/dL 0.8 mg/dL, 0.9 mg/dL, 1.0 mg/dL, 1.1 mg/dL, 1.2mg/dL, 1.3 mg/dL, 1.4 mg/dL, 1.5 mg/dL, 1.6 mg/dL, 1.7 mg/dL, 1.8 mg/dL,1.9 mg/dL, 2.0 mg/dL, 2.1 mg/dL, 2.2 mg/dL, 2.3 mg/dL, 2.4 mg/dL, 2.5mg/dL, 2.6 mg/dL, 2.7 mg/dL, 2.8 mg/dL, 2.9 mg/dL, 3.0 mg/dL, 3.1 mg/dL,3.2 mg/dL, 3.3 mg/dL, 3.4 mg/dL, 3.5 mg/dL, 3.6 mg/dL, 3.7 mg/dL, 3.8mg/dL, 3.9 mg/dL, or 4.0 mg/dL. In some instances, a transplant subjectwith a certain transplant condition (e.g., AR, ADNR, CAN, etc.) may havean increase of a serum creatinine level of at least 10%, 20%, 30%, 40%,50%, 60%, 70%, 80%, 90%, or 100% from baseline. In some instances, atransplant subject with a certain transplant condition (e.g., AR, ADNR,CAN, etc.) may have an increase of a serum creatinine level of at least1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold,or 10-fold from baseline. In some cases, the increase in serumcreatinine (e.g., any increase in the concentration of serum creatininedescribed herein) may occur over about 0.25 days, 0.5 days, 0.75 days, 1day, 1.25 days, 1.5 days, 1.75 days, 2.0 days, 3.0 days, 4.0 days, 5.0days, 6.0 days, 7.0 days, 8.0 days, 9.0 days, 10.0 days, 15 days, 30days, 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months, ormore. In some instances, a transplant subject with a particulartransplant condition (e.g., AR, ADNR, CAN, etc.) may have a decrease ofa eGFR of at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%from baseline. In some cases, the decrease in eGFR may occur over 0.25days, 0.5 days, 0.75 days, 1 day, 1.25 days, 1.5 days, 1.75 days, 2.0days, 3.0 days, 4.0 days, 5.0 days, 6.0 days, 7.0 days, 8.0 days, 9.0days, 10.0 days, 15 days, 30 days, 1 month, 2 months, 3 months, 4months, 5 months, or 6 months, or more. In some instances, diagnosing,predicting, or monitoring the status or outcome of a transplant orcondition comprises determining transplant recipient-specific baselinesand/or thresholds.

In some cases, the methods provided herein are used on a subject who hasnot yet received a transplant, such as a subject who is awaiting atissue or organ transplant. In other cases, the subject is a transplantdonor. In some cases, the subject has not received a transplant and isnot expected to receive such transplant. In some cases, the subject maybe a subject who is suffering from diseases requiring monitoring ofcertain organs for potential failure or dysfunction. In some cases, thesubject may be a healthy subject.

A transplant recipient may be a recipient of a solid organ or a fragmentof a solid organ. The solid organ may be a lung, kidney, heart, liver,pancreas, large intestine, small intestine, gall bladder, reproductiveorgan or a combination thereof. Preferably, the transplant recipient isa kidney transplant or allograft recipient. In some instances, thetransplant recipient may be a recipient of a tissue or cell. The tissueor cell may be amnion, skin, bone, blood, marrow, blood stem cells,platelets, umbilical cord blood, cornea, middle ear, heart valve, vein,cartilage, tendon, ligament, nerve tissue, embryonic stem (ES) cells,induced pluripotent stem cells (IPSCs), stem cells, adult stem cells,hematopoietic stem cells, or a combination thereof.

The donor organ, tissue, or cells may be derived from a subject who hascertain similarities or compatibilities with the recipient subject. Forexample, the donor organ, tissue, or cells may be derived from a donorsubject who is age-matched, ethnicity-matched, gender-matched,blood-type compatible, or HLA-type compatible with the recipientsubject.

The transplant recipient may be a male or a female. The transplantrecipient may be patients of any age. For example, the transplantrecipient may be a patient of less than about 10 years old. For example,the transplant recipient may be a patient of at least about 0, 5, 10,20, 30, 40, 50, 60, 70, 80, 90, or 100 years old. The transplantrecipient may be in utero. Often, the subject is a patient or otherindividual undergoing a treatment regimen, or being evaluated for atreatment regimen (e.g., immunosuppressive therapy). However, in someinstances, the subject is not undergoing a treatment regimen. A featureof the graft tolerant phenotype detected or identified by the subjectmethods is that it is a phenotype which occurs without immunosuppressivetherapy, e.g., it is present in a host that is not undergoingimmunosuppressive therapy such that immunosuppressive agents are notbeing administered to the host.

In various embodiments, the subjects suitable for methods of theinvention are patients who have undergone an organ transplant within 6hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 10 days, 15days, 20 days, 25 days, 1 month, 2 months, 3 months, 4 months, 5 months,7 months, 9 months, 11 months, 1 year, 2 years, 4 years, 5 years, 10years, 15 years, 20 years or longer of prior to receiving aclassification disclosed herein (e.g., a classification obtained by themethods disclosed herein). Some of the methods further comprise changingthe treatment regime of the patient responsive to the detecting,prognosing, diagnosing or monitoring step. In some of these methods, thesubject can be one who has received a drug before performing themethods, and the change in treatment comprises administering anadditional drug, administering a higher or lower dose of the same drug,stopping administration of the drug, or replacing the drug with adifferent drug or therapeutic intervention.

The subjects can include transplant recipients or donors or healthysubjects. The methods can be useful on human subjects who have undergonea kidney transplant although can also be used on subjects who have goneother types of transplant (e.g., heart, liver, lung, stem cell, etc.).The subjects may be mammals or non-mammals. The methods can be useful onnon-humans who have undergone kidney or other transplant. Preferably,the subjects are a mammal, such as, a human, non-human primate (e.g.,apes, monkeys, chimpanzees), cat, dog, rabbit, goat, horse, cow, pig,rodent, mouse, SCID mouse, rat, guinea pig, or sheep. Even morepreferably, the subject is a human. The subject may be male or female;the subject may be a fetus, infant, child, adolescent, teenager oradult.

In some methods, species variants or homologs of these genes can be usedin a non-human animal model. Species variants may be the genes indifferent species having greatest sequence identity and similarity infunctional properties to one another. Many of such species variantshuman genes may be listed in the Swiss-Prot database.

Samples

Methods for detecting molecules (e.g., nucleic acids, proteins, etc.) ina subject who has received a transplant (e.g., organ transplant, tissuetransplant, stem cell transplant) in order to detect, diagnose, monitor,predict, or evaluate the status or outcome of the transplant aredescribed in this disclosure. In some cases, the molecules arecirculating molecules. In some cases, the molecules are expressed inblood cells. In some cases, the molecules are cell-free circulatingnucleic acids.

The methods, kits, and systems disclosed herein may be used to classifyone or more samples from one or more subjects. A sample may be anymaterial containing tissues, cells, nucleic acids, genes, genefragments, expression products, polypeptides, exosomes, gene expressionproducts, or gene expression product fragments of a subject to betested. Methods for determining sample suitability and/or adequacy areprovided. A sample may include but is not limited to, tissue, cells, orbiological material from cells or derived from cells of an individual.The sample may be a heterogeneous or homogeneous population of cells ortissues. In some cases, the sample is from a single patient. In somecases, the method comprises analyzing multiple samples at once, e.g.,via massively parallel sequencing.

The sample is preferably a bodily fluid. The bodily fluid may be sweat,saliva, tears, urine, blood, menses, semen, and/or spinal fluid. Inpreferred embodiments, the sample is a blood sample. The sample maycomprise one or more peripheral blood lymphocytes. The sample may be awhole blood sample. The blood sample may be a peripheral blood sample.In some cases, the sample comprises peripheral blood mononuclear cells(PBMCs); in some cases, the sample comprises peripheral bloodlymphocytes (PBLs). The sample may be a serum sample. In some instances,the sample is a tissue sample or an organ sample, such as a biopsy.

The methods, kits, and systems disclosed herein may comprisespecifically detecting, profiling, or quantitating molecules (e.g.,nucleic acids, DNA, RNA, polypeptides, etc.) that are within thebiological samples. In some instances, genomic expression products,including RNA, or polypeptides, may be isolated from the biologicalsamples. In some cases, nucleic acids, DNA, RNA, polypeptides may beisolated from a cell-free source. In some cases, nucleic acids, DNA,RNA, polypeptides may be isolated from cells derived from the transplantrecipient.

The sample may be obtained using any method known to the art that canprovide a sample suitable for the analytical methods described herein.The sample may be obtained by a non-invasive method such as a throatswab, buccal swab, bronchial lavage, urine collection, scraping of theskin or cervix, swabbing of the cheek, saliva collection, fecescollection, menses collection, or semen collection. The sample may beobtained by a minimally-invasive method such as a blood draw. The samplemay be obtained by venipuncture. In other instances, the sample isobtained by an invasive procedure including but not limited to: biopsy,alveolar or pulmonary lavage, or needle aspiration. The method of biopsymay include surgical biopsy, incisional biopsy, excisional biopsy, punchbiopsy, shave biopsy, or skin biopsy. The sample may be formalin fixedsections. The method of needle aspiration may further include fineneedle aspiration, core needle biopsy, vacuum assisted biopsy, or largecore biopsy. In some embodiments, multiple samples may be obtained bythe methods herein to ensure a sufficient amount of biological material.In some instances, the sample is not obtained by biopsy. In someinstances, the sample is not a kidney biopsy.

Sample Data

The methods, kits, and systems disclosed herein may comprise datapertaining to one or more samples or uses thereof. The data may beexpression level data. The expression level data may be determined bymicroarray, SAGE, sequencing, blotting, or PCR amplification (e.g.RT-PCR, quantitative PCR, etc.). In some cases, the expression level isdetermined by sequencing (e.g., RNA or DNA sequencing). The expressionlevel data may be determined by microarray. Exemplary microarraysinclude but are not limited to the Affymetrix Human Genome U133 Plus 2.0GeneChip or the HT HG-U133+ PM Array Plate.

In some cases, arrays (e.g., Illumina arrays) may use different probesattached to different particles or beads. In such arrays, the identityof which probe is attached to which particle or beads is usuallydeterminable from an encoding system. The probes can beoligonucleotides. In some cases, the probes may comprise several matchprobes with perfect complementarity to a given target mRNA, optionallytogether with mismatch probes differing from the match probes. See,e.g., (Lockhart, et al., Nature Biotechnology 14:1675-1680 (1996); andLipschutz, et al., Nature Genetics Supplement 21: 20-24, 1999). Sucharrays may also include various control probes, such as a probecomplementary to a housekeeping gene likely to be expressed in mostsamples. Regardless of the specifics of array design, an array generallycontains one or more probes either perfectly complementary to aparticular target mRNA or sufficiently complementary to the target mRNAto distinguish it from other mRNAs in the sample. The presence of such atarget mRNA can be determined from the hybridization signal of suchprobes, optionally by comparison with mismatch or other control probesincluded in the array. Typically, the target bears a fluorescent label,in which case hybridization intensity can be determined by, for example,a scanning confocal microscope in photon counting mode. Appropriatescanning devices are described by e.g., U.S. Pat. No. 5,578,832, andU.S. Pat. No. 5,631,734. The intensity of labeling of probes hybridizingto a particular mRNA or its amplification product may provide a rawmeasure of expression level.

The data pertaining to the sample may be compared to data pertaining toone or more control samples, which may be samples from the same patientat different times. In some cases, the one or more control samples maycomprise one or more samples from healthy subjects, unhealthy subjects,or a combination thereof. The one or more control samples may compriseone or more samples from healthy subjects, subjects suffering fromtransplant dysfunction with no rejection, subjects suffering fromtransplant rejection, or a combination thereof. The healthy subjects maybe subjects with normal transplant function. The data pertaining to thesample may be sequentially compared to two or more classes of samples.The data pertaining to the sample may be sequentially compared to threeor more classes of samples. The classes of samples may comprise controlsamples classified as being from subjects with normal transplantfunction, control samples classified as being from subjects sufferingfrom transplant dysfunction with no rejection, control samplesclassified as being from subjects suffering from transplant rejection,or a combination thereof.

Biomarkers/Gene Expression Products

Biomarker refers to a measurable indicator of some biological state orcondition. In some instances, a biomarker can be a substance found in asubject, a quantity of the substance, or some other indicator. Forexample, a biomarker may be the amount of RNA, mRNA, tRNA, miRNA,mitochondrial RNA, siRNA, polypeptides, proteins, DNA, cDNA and/or othergene expression products in a sample. In some instances, gene expressionproducts may be proteins or RNA. In some instances, RNA may be anexpression product of non-protein coding genes such as ribosomal RNA(rRNA), transfer RNA (tRNA), micro RNA (miRNA), or small nuclear RNA(snRNA) genes. In some instances, RNA may be messenger RNA (mRNA). Incertain examples, a biomarker or gene expression product may be DNAcomplementary or corresponding to RNA expression products in a sample.

The methods, compositions and systems as described here also relate tothe use of biomarker panels and/or gene expression products for purposesof identification, diagnosis, classification, treatment or to otherwisecharacterize various conditions of organ transplant comprising AR, ANDR,TX, IFTA, CAN, SCAR, hepatitis C virus recurrence (HCV-R). Sets ofbiomarkers and/or gene expression products useful for classifyingbiological samples are provided, as well as methods of obtaining suchsets of biomarkers. Often, the pattern of levels of gene expressionbiomarkers in a panel (also known as a signature) is determined and thenused to evaluate the signature of the same panel of biomarkers in asample, such as by a measure of similarity between the sample signatureand the reference signature. In some instances, biomarker panels or geneexpression products may be chosen to distinguish acute rejection (AR)from transplant dysfunction with no acute rejection (ADNR) expressionprofiles. In some instances, biomarker panels or gene expressionproducts may be chosen to distinguish acute rejection (AR) from normallyfunctioning transplant (TX) expression profiles. In some instances,biomarker panels or gene expression products may be selected todistinguish acute dysfunction with no transplant rejection (ADNR) fromnormally functioning transplant (TX) expression profiles. In someinstances, biomarker panels or gene expression products may be selectedto distinguish transplant dysfunction from acute rejection (AR)expression profiles. In certain examples, this disclosure providesmethods of reclassifying an indeterminate biological sample fromsubjects into a healthy, acute rejection or acute dysfunction norejection categories, and related kits, compositions and systems.

The expression level may be normalized. In some instances, normalizationmay comprise quantile normalization. Normalization may comprise frozenrobust multichip average (fRMA) normalization.

Determining the expression level may comprise normalization by frozenrobust multichip average (fRMA). Determining the expression level maycomprise reverse transcribing the RNA to produce cRNA.

The methods provided herein may comprise identifying a condition fromone or more gene expression products from Table 1a, 1b, 1c, 1d, 8, 9,10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some cases, ARof a kidney transplant (or other organ transplant) can be detected fromone or more gene expression products from Table 1a, 1b, 1c, 1d, 8, 10b,or 12b, in any combination. In some cases, ADNR of a kidney transplant(or other organ transplant) can be detected from one or more geneexpression products from Table 1a, 1b, 1c, 1d, 10b, or 12b, in anycombination. In some cases, TX (or normal functioning) of a kidneytransplant (or other organ transplant) can be detected from one or moregene expression products from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, or14b, in any combination. In some cases, SCAR of kidney transplant (orother organ transplant) can be detected from one or more gene expressionproducts from Table 8 or 9, in any combination. In some instances, AR ofa liver transplant (or other organ transplant) can be detected from oneor more gene expression products from Table 16b, 17b, or 18b, in anycombination. In some instances, ADNR of liver can be detected from oneor more gene expression products from Table 16b. In some cases, TX ofliver can be detected from one or more gene expression products fromTable 16b. In some cases, HCV of liver can be detected from one or moregene expression products from Table 17b or 18b, in any combination. Insome cases, HCV+AR of liver can be detected from one or more geneexpression products from Table 17b or 18b, in any combination.

The methods provided herein may also comprise identifying a conditionfrom one or more gene expression products from a tissue biopsy sample.From example, AR of kidney biopsy can be detected from one or more geneexpression products from Table 10b or 12b, in any combination. ADNR ofkidney biopsy can be detected from one or more gene expression productsfrom Table 10b or 12b, in any combination. CAN of kidney biopsy can bedetected from one or more gene expression products from Table 12b or14b, in any combination. TX of kidney biopsy can be detected from one ormore gene expression products from Table 10b, 12b, or 14b, in anycombination. AR of liver biopsy can be detected from one or more geneexpression products from Table 18b. HCV of liver biopsy can be detectedfrom one or more gene expression products from Table 18b. HCV+AR ofliver biopsy can be detected from one or more gene expression productsfrom Table 18b.

The gene expression product may be a peptide or RNA. At least one of thegene expression products may correspond to a gene found in Table 1a. Thegene expression product may be a peptide or RNA. At least one of thegene expression products may correspond to a gene found in Table 1c. Atleast one of the gene expression products may correspond to a gene foundin Table 1a, 1b, 1c or 1d, in any combination. At least one of the geneexpression products may correspond to a gene found in Table 1a, 1b, 1c,1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The geneexpression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1a. Thegene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table1c. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found inTable 1a, 1b, 1c, or 1d, in any combination. The gene expressionproducts may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1a, 1b, 1c, 1d,8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The geneexpression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80,90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genesfound in Table 1a. The gene expression products may correspond to 10,20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170,180, 190, 200 or more genes found in Table 1c. The gene expressionproducts may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table1a, 1b, 1c, or 1d, in any combination. The gene expression products maycorrespond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1a, 1b,1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Thegene expression products may correspond to 10, 20, 30, 40, 50, 60, 70,80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or lessgenes found in Table 1a. The gene expression products may correspond to10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160,170, 180, 190, 200 or less genes found in Table 1c. The gene expressionproducts may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table1a, 1b, 1c, or 1d, in any combination. The gene expression products maycorrespond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1a, 1b,1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Thegene expression products may correspond to 100, 200, 300, 400, 500, 600,700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,1900, 2000 or more genes found in Table 1a. The gene expression productsmay correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000,1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genesfound in Table 1c. The gene expression products may correspond to 100,200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400,1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1a, 1b,1c, or 1d, in any combination. The gene expression products maycorrespond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100,1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes foundin Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in anycombination. The gene expression products may correspond to 10 or moregenes found in Table 1a. The gene expression products may correspond to10 or more genes found in Table 1c. The gene expression products maycorrespond to 10 or more genes found in Table 1a, 1b, 1c, or 1d, in anycombination. The gene expression products may correspond to 10 or moregenes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or18b, in any combination. The gene expression products may correspond to25 or more genes found in Table 1a. The gene expression products maycorrespond to 25 or more genes found in Table 1c. The gene expressionproducts may correspond to 25 or more genes found in Table 1a, 1b, 1c,or 1d, in any combination. The gene expression products may correspondto 25 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b,16b, 17b, or 18b, in any combination. The gene expression products maycorrespond to 50 or more genes found in Table 1a. The gene expressionproducts may correspond to 50 or more genes found in Table 1c. The geneexpression products may correspond to 50 or more genes found in Table1a, 1b, 1c, or 1d, in any combination. The gene expression products maycorrespond to 50 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b,12b, 14b, 16b, 17b, or 18b, in any combination. The gene expressionproducts may correspond to 100 or more genes found in Table 1a. The geneexpression products may correspond to 100 or more genes found in Table1c. The gene expression products may correspond to 100 or more genesfound in Table 1a, 1b, 1c, or 1d, in any combination. The geneexpression products may correspond to 100 or more genes found in Table1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in anycombination. The gene expression products may correspond to 200 or moregenes found in Table 1a. The gene expression products may correspond to200 or more genes found in Table 1c. The gene expression products maycorrespond to 200 or more genes found in Table 1a, 1b, 1c, or 1d in anycombination. The gene expression products may correspond to 200 or moregenes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or18b, in any combination.

At least a subset the gene expression products may correspond to thegenes found in Table 1a. At least a subset the gene expression productsmay correspond to the genes found in Table 1c. At least a subset thegene expression products may correspond to the genes found in Table 1a,1b, 1c, or 1d, in any combination. At least a subset the gene expressionproducts may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8,9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%,17%, 18%, 19%, 20% or more of the gene expression products maycorrespond to the genes found in Table 1a. At least about 1%, 2%, 3%,4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%,19%, 20% or more of the gene expression products may correspond to thegenes found in Table 1c. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%,9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of thegene expression products may correspond to the genes found in Table 1a,1b, 1c, or 1d, in any combination. At least about 1%, 2%, 3%, 4%, 5%,6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% ormore of the gene expression products may correspond to the genes foundin Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in anycombination. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%,55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the geneexpression products may correspond to the genes found in Table 1a. Atleast about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%,70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expressionproducts may correspond to the genes found in Table 1c. At least about10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%,80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products maycorrespond to the genes found in Table 1a, 1b, 1c, or 1d, in anycombination. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%,55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the geneexpression products may correspond to the genes found in Table 1a, 1b,1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Atleast about 5% of the gene expression products may correspond to thegenes found in Table 1a. At least about 5% of the gene expressionproducts may correspond to the genes found in Table 1c. At least about5% of the gene expression products may correspond to the genes found inTable 1a, 1b, 1c, or 1d, in any combination. At least about 5% of thegene expression products may correspond to the genes found in Table 1a,1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.At least about 10% of the gene expression products may correspond to thegenes found in Table 1a. At least about 10% of the gene expressionproducts may correspond to the genes found in Table 1c. At least about10% of the gene expression products may correspond to the genes found inTable 1a, 1b, 1c, or 1d, in any combination. At least about 10% of thegene expression products may correspond to the genes found in Table 1a,1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.At least about 25% of the gene expression products may correspond to thegenes found in Table 1a. At least about 25% of the gene expressionproducts may correspond to the genes found in Table 1c. At least about25% of the gene expression products may correspond to the genes found inTable 1a, 1b, 1c, or 1d, in any combination. At least about 25% of thegene expression products may correspond to the genes found in Table 1a,1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.At least about 30% of the gene expression products may correspond to thegenes found in Table 1a. At least about 30% of the gene expressionproducts may correspond to the genes found in Table 1c. At least about30% of the gene expression products may correspond to the genes found inTable 1a, 1b, 1c, or 1d, in any combination. At least about 30% of thegene expression products may correspond to the genes found in Table 1a,1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

In another aspect, the invention provides arrays, which contain asupport or supports bearing a plurality of nucleic acid probescomplementary to a plurality of mRNAs fewer than 5000 in number.Typically, the plurality of mRNAs includes mRNAs expressed by at leastfive genes selected from Table 1a. In another embodiment, the pluralityof mRNAs includes mRNAs expressed by at least five genes selected fromTable 1c. The plurality of mRNAs may also include mRNAs expressed by atleast five genes selected from Table 1a, 1b, 1c, or 1d, in anycombination. The plurality of mRNAs may also include mRNAs expressed byat least five genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b,14b, 16b, 17b, or 18b, in any combination. In some embodiments, theplurality of mRNAs are fewer than 1000 or fewer than 100 in number. Insome embodiments, the plurality of nucleic acid probes are attached to aplanar support or to beads. In a related aspect, the invention providesarrays that contain a support or supports bearing a plurality of ligandsthat specifically bind to a plurality of proteins fewer than 5000 innumber. The plurality of proteins typically includes at least fiveproteins encoded by genes selected from Table 1a. The plurality ofproteins typically includes at least five proteins encoded by genesselected from Table 1c. The plurality of proteins typically includes atleast five proteins encoded by genes selected from Table 1a, 1b, 1c, or1d, in any combination. The plurality of proteins typically includes atleast five proteins encoded by genes selected from Table 1a, 1b, 1c, 1d,8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In someembodiments, the plurality of proteins are fewer than 1000 or fewer than100 in number. In some embodiments, the plurality of ligands areattached to a planar support or to beads. In some embodiments, the atleast five proteins are encoded by genes selected from Table 1a. In someembodiments, the at least five proteins are encoded by genes selectedfrom Table 1c. In some embodiments, the at least five proteins areencoded by genes selected from Table 1a, 1b, 1c, or 1d, in anycombination. In some embodiments, the at least five proteins are encodedby genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b,17b, or 18b, in any combination. In some embodiments, the ligands aredifferent antibodies that bind to different proteins of the plurality ofproteins.

Methods, kits, and systems disclosed herein may have a plurality ofgenes associated with one or more biomarkers selected from geneexpression products corresponding to genes listed in Table 1a. Methods,kits, and systems disclosed herein may have a plurality of genesassociated with one or more biomarkers selected from gene expressionproducts corresponding to genes listed in Table 1c. Methods, kits, andsystems disclosed herein may also have a plurality of genes associatedwith one or more biomarkers selected from gene expression productscorresponding to genes listed in Table 1a, 1b, 1c, or 1d, in anycombination. Methods, kits, and systems disclosed herein may also have aplurality of genes associated with one or more biomarkers selected fromgene expression products corresponding to genes listed in Table 1a, 1b,1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Insome instances, there may be genes selected from 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15 or 16 or more biomarker panels and can havefrom 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170,180, 190, 200 or more gene expression products from each biomarkerpanel, in any combination. In some instances, the biomarkers within eachpanel are interchangeable (modular). The plurality of biomarkers in allpanels can be substituted, increased, reduced, or improved toaccommodate the classification system described herein. In someembodiments, the set of genes combined give a specificity or sensitivityof greater than 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.5%, or a positive predictivevalue or negative predictive value of at least 95%, 95.5%, 96%, 96.5%,97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more.

Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1a. Classifiers maycomprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20 biomarkers disclosed in Table 1c. Classifiers may comprise 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination.Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8,9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Classifiers maycomprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1a.Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed inTable 1c. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90,100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkersdisclosed in Table 1a, 1b, 1c, or 1d, in any combination. Classifiersmay comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1a, 1b,1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900,1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000biomarkers disclosed in Table 1a. Classifiers may comprise 100, 200,300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500,1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1c.Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900,1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination.Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900,1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b,17b, or 18b, in any combination.

At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%,14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from theclassifiers may be selected from biomarkers disclosed in Table 1a. Atleast about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%,15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiersmay be selected from biomarkers disclosed in Table 1c. At least about1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%,17%, 18%, 19%, or 20% of the biomarkers from the classifiers may beselected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in anycombination. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%,11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkersfrom the classifiers may be selected from biomarkers disclosed in Table1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in anycombination. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%,45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%,80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from theclassifiers may be selected from biomarkers disclosed in Table 1a. Atleast about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%,52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%,87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may beselected from biomarkers disclosed in Table 1c. At least about 22%, 25%,27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%,62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or97% of the biomarkers from the classifiers may be selected frombiomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. Atleast about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%,52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%,87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may beselected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b,12b, 14b, 16b, 17b, or 18b, in any combination. At least about 3% of thebiomarkers from the classifiers may be selected from biomarkersdisclosed in Table 1a. At least about 3% of the biomarkers from theclassifiers may be selected from biomarkers disclosed in Table 1c. Atleast about 3% of the biomarkers from the classifiers may be selectedfrom biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in anycombination. At least about 3% of the biomarkers from the classifiersmay be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9,10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 5%of the biomarkers from the classifiers may be selected from biomarkersdisclosed in Table 1a. At least about 5% of the biomarkers from theclassifiers may be selected from biomarkers disclosed in Table 1c. Atleast about 5% of the biomarkers from the classifiers may be selectedfrom biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in anycombination. At least about 5% of the biomarkers from the classifiersmay be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9,10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 10%of the biomarkers from the classifiers may be selected from biomarkersdisclosed in Table 1a. At least about 10% of the biomarkers from theclassifiers may be selected from biomarkers disclosed in Table 1c. Atleast about 10% of the biomarkers from the classifiers may be selectedfrom biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in anycombination. At least about 10% of the biomarkers from the classifiersmay be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9,10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

Classifier probe sets may comprise one or more oligonucleotides. Theoligonucleotides may comprise at least a portion of a sequence that canhybridize to one or more biomarkers from the panel of biomarkers.Classifier probe sets may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20 or more oligonucleotides, wherein atleast a portion of the oligonucleotide can hybridize to at least aportion of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20 or more biomarkers from the panel of biomarkers.Classifier probe sets may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90,100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or moreoligonucleotides, wherein at least a portion of the oligonucleotide canhybridize to at least a portion of at least 10, 20, 30, 40, 50, 60, 70,80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or morebiomarkers from the panel of biomarkers. Classifier probe sets maycomprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,150, 160, 170, 180, 190, 200 or fewer oligonucleotides, wherein at leasta portion of the oligonucleotide can hybridize to fewer than 10, 20, 30,40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,190, 200 or more biomarkers from the panel of biomarkers. Classifierprobe sets may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900,1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or moreoligonucleotides, wherein at least a portion of the oligonucleotide canhybridize to at least a portion of at least 100, 200, 300, 400, 500,600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700,1800, 1900, 2000 or more biomarkers from the panel of biomarkers.

Training of multi-dimensional classifiers (e.g., algorithms) may beperformed on numerous samples. For example, training of themulti-dimensional classifier may be performed on at least about 10, 20,30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,190, 200 or more samples. Training of the multi-dimensional classifiermay be performed on at least about 200, 210, 220, 230, 240, 250, 260,270, 280, 290, 300, 350, 400, 450, 500 or more samples. Training of themulti-dimensional classifier may be performed on at least about 525,550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300,1400, 1500, 1600, 1700, 1800, 2000 or more samples.

The total sample population may comprise samples obtained byvenipuncture. Alternatively, the total sample population may comprisesamples obtained by venipuncture, needle aspiration, fine needleaspiration, or a combination thereof. The total sample population maycomprise samples obtained by venipuncture, needle aspiration, fineneedle aspiration, core needle biopsy, vacuum assisted biopsy, largecore biopsy, incisional biopsy, excisional biopsy, punch biopsy, shavebiopsy, skin biopsy, or a combination thereof. In some embodiments, thesamples are not obtained by biopsy. The percent of the total samplepopulation that is obtained by venipuncture may be greater than about1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 50%, 55%, 60%, 65%, 70%, 75%,80%, 85%, 90%, or 95%. The percent of the total sample population thatis obtained by venipuncture may be greater than about 1%. The percent ofthe total sample population that is obtained by venipuncture may begreater than about 5%. The percent of the total sample population thatis obtained by venipuncture may be greater than about 10%

There may be a specific (or range of) difference in gene expressionbetween subtypes or sets of samples being compared to one another. Insome examples, the gene expression of some similar subtypes are mergedto form a super-class that is then compared to another subtype, oranother super-class, or the set of all other subtypes. In someembodiments, the difference in gene expression level is at least about5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% or more. In someembodiments, the difference in gene expression level is at least about2, 3, 4, 5, 6, 7, 8, 9, 10 fold or more.

The present invention may initialize gene expression productscorresponding to one or more biomarkers selected from gene expressionproducts derived from genes listed in Table 1a. The present inventionmay initialize gene expression products corresponding to one or morebiomarkers selected from gene expression products derived from geneslisted in Table 1c. The present invention may initialize gene expressionproducts corresponding to one or more biomarkers selected from geneexpression products derived from genes listed in Table 1a, 1b, 1c, or1d, in any combination. The present invention may initialize geneexpression products corresponding to one or more biomarkers selectedfrom gene expression products derived from genes listed in Table 1a, 1b,1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Themethods, compositions and systems provided herein may include expressionproducts corresponding to any or all of the biomarkers selected fromgene expression products derived from genes listed in Table 1a, as wellas any subset thereof, in any combination. The methods, compositions andsystems provided herein may include expression products corresponding toany or all of the biomarkers selected from gene expression productsderived from genes listed in Table 1c, as well as any subset thereof, inany combination. The methods, compositions and systems provided hereinmay include expression products corresponding to any or all of thebiomarkers selected from gene expression products derived from geneslisted in Table 1a, 1b, 1c, or 1d, in any combination, as well as anysubset thereof, in any combination. The methods, compositions andsystems provided herein may include expression products corresponding toany or all of the biomarkers selected from gene expression productsderived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b,16b, 17b, or 18b, in any combination, as well as any subset thereof, inany combination. For example, the methods may use gene expressionproducts corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1a. Inanother embodiment, the methods use gene expression productscorresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70,75, 80, 85, 90, 95, or 100 of the markers provided Table 1c. In anotherexample, the methods may use gene expression products corresponding toat least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90,95, or 100 of the markers provided Table 1a, 1b, 1c, or 1d, in anycombination. In another example, the methods may use gene expressionproducts corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1a, 1b,1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Themethods may use gene expression products corresponding to at least about110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240,250, 260, 270, 280, 290, 300 or more of the markers provided in geneexpression products derived from genes listed in Table 1a. The methodsmay use gene expression products corresponding to at least about 110,120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250,260, 270, 280, 290, 300 or more of the markers provided in geneexpression products derived from genes listed in Table 1c. The methodsmay use gene expression products corresponding to at least about 110,120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250,260, 270, 280, 290, 300 or more of the markers provided in geneexpression products derived from genes listed in Table 1a, 1b, 1c, or1d, in any combination. The methods may use gene expression productscorresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180,190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more ofthe markers provided in gene expression products derived from geneslisted in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b,in any combination.

Further disclosed herein are classifier sets and methods of producingone or more classifier sets. The classifier set may comprise one or moregenes. The classifier set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes. The classifier setmay comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,140, 150, 160, 170, 180, 190, 200 or more genes. The classifier set maycomprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200,1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes. Theclassifier set may comprise 1000, 2000, 3000, 4000, 5000, 6000, 7000,8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000,18000, 19000, 20000 or more genes. The classifier set may comprise10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000,110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000,200000 or more genes. The classifier set may comprise 10 or more genes.The classifier set may comprise 30 or more genes. The classifier set maycomprise 60 or more genes. The classifier set may comprise 100 or moregenes. The classifier set may comprise 125 or more genes. The classifierset may comprise 150 or more genes. The classifier set may comprise 200or more genes. The classifier set may comprise 250 or more genes. Theclassifier set may comprise 300 or more genes.

The classifier set may comprise one or more differentially expressedgenes. The classifier set may comprise one or more differentiallyexpressed genes. The classifier set may comprise 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more differentiallyexpressed genes. The classifier set may comprise 10, 20, 30, 40, 50, 60,70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 ormore differentially expressed genes. The classifier set may comprise100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300,1400, 1500, 1600, 1700, 1800, 1900, 2000 or more differentiallyexpressed genes. The classifier set may comprise 1000, 2000, 3000, 4000,5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000,16000, 17000, 18000, 19000, 20000 or more differentially expressedgenes. The classifier set may comprise 10000, 20000, 30000, 40000,50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000,140000, 150000, 160000, 170000, 180000, 190000, 200000 or moredifferentially expressed genes. The classifier set may comprise 10 ormore differentially expressed genes. The classifier set may comprise 30or more differentially expressed genes. The classifier set may comprise60 or more differentially expressed genes. The classifier set maycomprise 100 or more differentially expressed genes. The classifier setmay comprise 125 or more differentially expressed genes. The classifierset may comprise 150 or more differentially expressed genes. Theclassifier set may comprise 200 or more differentially expressed genes.The classifier set may comprise 250 or more differentially expressedgenes. The classifier set may comprise 300 or more differentiallyexpressed genes.

In some instances, the method provides a number, or a range of numbers,of biomarkers or gene expression products that are used to characterizea sample. Examples of classification panels may be derived from geneslisted in Table 1a. Examples of classification panels may be derivedfrom genes listed in Table 1c. Examples of classification panels may bederived from genes listed in Table 1a, 1b, 1c, or 1d, in anycombination. Examples of classification panels may be derived from geneslisted in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b,in any combination. However, the present disclosure is not meant to belimited solely to the biomarkers disclosed herein. Rather, it isunderstood that any biomarker, gene, group of genes or group ofbiomarkers identified through methods described herein is encompassed bythe present invention. In some embodiments, the method involvesmeasuring (or obtaining) the levels of two or more gene expressionproducts that are within a biomarker panel and/or within aclassification panel. For example, in some embodiments, a biomarkerpanel or a gene expression product may contain at least about 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30,33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75,77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132,138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185,187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280,290 or 300 or more genes chosen from Table 1a. In some embodiments, abiomarker panel or a gene expression product may contain at least about1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70,72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122,128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180,183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260,270, 280, 290 or 300 or more genes chosen from Table 1c. In someembodiments, a biomarker panel or a gene expression product may containat least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60,62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110,113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165,170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230,240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a,1b, 1c, or 1d, in any combination. In some embodiments, a biomarkerpanel or a gene expression product may contain at least about 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30,33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75,77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132,138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185,187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280,290 or 300 or more genes chosen from Table 1a, 1b, 1c, 1d, 8, 9, 10b,12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, abiomarker panel or a gene expression product may contain no more thanabout 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67,70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117,122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175,180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250,260, 270, 280, 290 or 300 or more genes chosen from Table 1a. In someembodiments, a biomarker panel or a gene expression product may containno more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57,60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107,110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160,165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220,230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table1c. In some embodiments, a biomarker panel or a gene expression productmay contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50,52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100,103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150,155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200,210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosenfrom Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, abiomarker panel or a gene expression product may contain no more thanabout 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67,70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117,122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175,180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250,260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c,1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In otherembodiments, a biomarker panel or a gene expression product may containabout 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67,70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117,122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175,180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250,260, 270, 280, 290 or 300 total genes chosen from Table 1a. In otherembodiments, a biomarker panel or a gene expression product may containabout 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67,70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117,122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175,180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250,260, 270, 280, 290 or 300 total genes chosen from Table 1c. In otherembodiments, a biomarker panel or a gene expression product may containabout 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67,70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117,122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175,180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250,260, 270, 280, 290 or 300 total genes chosen from Table 1a, 1b, 1c, or1d, in any combination. In other embodiments, a biomarker panel or agene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45,47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95,97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145,147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195,197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total geneschosen from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b,in any combination.

Measuring Expression Levels

The methods, kits and systems disclosed herein may be used to obtain orto determine an expression level for one or more gene products in asubject. In some instances, the expression level is used to develop ortrain an algorithm or classifier provided herein. In some instances,where the subject is a patient, such as a transplant recipient; geneexpression levels are measured in a sample from the transplant recipientand a classifier or algorithm (e.g., trained algorithm) is applied tothe resulting data in order to detect, predict, monitor, or estimate therisk of a transplant condition (e.g., acute rejection).

The expression level of the gene products (e.g., RNA, cDNA,polypeptides) may be determined using any method known in the art. Insome instances, the expression level of the gene products (e.g., nucleicacid gene products such as RNA) is measured by microarray, sequencing,electrophoresis, automatic electrophoresis, SAGE, blotting, polymerasechain reaction (PCR), digital PCR, RT-PCR, and/or quantitative PCR(qPCR). In certain preferred embodiments, the expression level isdetermined by microarray. For example, the microarray may be anAffymetrix Human Genome U133 Plus 2.0 GeneChip or a HT HG-U133+ PM ArrayPlate.

In certain preferred embodiments, the expression level of the geneproducts (e.g., RNA) is determined by sequencing, such as by RNAsequencing or by DNA sequencing (e.g., of cDNA generated fromreverse-transcribing RNA (e.g., mRNA) from a sample). Sequencing may beperformed by any available method or technique. Sequencing methods mayinclude: high-throughput sequencing, pyrosequencing, classic Sangarsequencing methods, sequencing-by-ligation, sequencing by synthesis,sequencing-by-hybridization, RNA-Seq (Illumina), Digital Gene Expression(Helicos), next generation sequencing, single molecule sequencing bysynthesis (SMSS) (Helicos), Ion Torrent Sequencing Machine (LifeTechnologies/Thermo-Fisher), massively-parallel sequencing, clonalsingle molecule Array (Solexa), shotgun sequencing, Maxim-Gilbertsequencing, primer walking, and any other sequencing methods known inthe art.

Measuring gene expression levels may comprise reverse transcribing RNA(e.g., mRNA) within a sample in order to produce cDNA. The cDNA may thenbe measured using any of the methods described herein (e.g., PCR,digital PCR, qPCR, microarray, SAGE, blotting, sequencing, etc.). Insome instances, the method may comprise reverse transcribing RNAoriginating from the subject (e.g., transplant recipient) to producecDNA, which is then measured such as by microarray, sequencing, PCR,and/or any other method available in the art.

In some instances, the gene products may be polypeptides. In suchinstances, the methods may comprise measuring polypeptide gene products.Methods of measuring or detecting polypeptides may be accomplished usingany method or technique known in the art. Examples of such methodsinclude proteomics, expression proteomics, mass spectrometry, 2D PAGE,3D PAGE, electrophoresis, proteomic chips, proteomic microarrays, and/orEdman degradation reactions.

The expression level may be normalized (e.g., signal normalization). Insome instances, signal normalization (e.g., quantile normalization) isperformed on an entire cohort. In general, quantile normalization is atechnique for making two or more distributions identical in statisticalproperties. However, in settings where samples must be processedindividually or in small batches, data sets that are normalizedseparately are generally not comparable. In some instances providedherein, the expression level of the gene products is normalized usingfrozen RMA (fRMA). fRMA is particularly useful because it overcomesthese obstacles by normalization of individual arrays to large publiclyavailable microarray databases allowing for estimates of probe-specificeffects and variances to be pre-computed and “frozen” (McCall et al.2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMCbioinformatics, 12:369). In some instances, a method provided hereindoes not comprise performing a normalization step. In some instances, amethod provided herein does not comprise performing quantilenormalization. In some cases, the normalization does not comprisequantile normalization. In certain preferred embodiments, the methodscomprise frozen robust multichip average (fRMA) normalization.

In some cases, analysis of expression levels initially provides ameasurement of the expression level of each of several individual genes.The expression level can be absolute in terms of a concentration of anexpression product, or relative in terms of a relative concentration ofan expression product of interest to another expression product in thesample. For example, relative expression levels of genes can beexpressed with respect to the expression level of a house-keeping genein the sample. Relative expression levels can also be determined bysimultaneously analyzing differentially labeled samples hybridized tothe same array. Expression levels can also be expressed in arbitraryunits, for example, related to signal intensity.

Biomarker Discovery and Validation

Exemplary workflows for cohort and bootstrapping strategies forbiomarker discovery and validation are depicted in FIG. 3. As shown inFIG. 3, the cohort-based method of biomarker discovery and validation isoutlined by the solid box and the bootstrapping method of biomarkerdiscovery and validation is outlined in the dotted box. For thecohort-based method, samples for acute rejection (n=63) (310), acutedysfunction no rejection (n=39) (315), and normal transplant function(n=46) (320) are randomly split into a discovery cohort (n=75) (325) anda validation cohort (n=73) (345). The samples from the discovery cohortare analyzed using a 3-class univariate F-test (1000 randompermutations, FDR <10%; BRB ArrayTools) (330). The 3-class univariateF-test analysis of the discovery cohort yielded 2977 differentiallyexpressed probe sets (Table 1) (335). Algorithms such as the NearestCentroid, Diagonal Linear Discriminant Analysis, and Support VectorMachines, are used to create a 3-way classifier for AR, ADNR and TX inthe discovery cohort (340). The 25-200 classifiers are “locked” (350).The “locked” classifiers are validated by samples from the validationcohort (345). For the bootstrapping method, 3-class univariate F-test isperformed on the whole data set of samples (n=148) (1000 randompermutations, FDR <10%; BRB ArrayTools) (355). The significantlyexpressed genes are selected to produce a probe set (n=200, based on thenearest centroid (NC), diagonal linear discriminant analysis (DLDA), orsupport vector machines (SVM)). Optimism-corrected AUCs are obtained forthe 200-probe set classifier discovered with the 2 cohort-based strategy(360). AUCs are obtained for the full data set (365). Optimism-correctedAUCs are obtained for the 200-probe set classifier by Bootstrapping from1000 samplings of the full data set with replacement (370).Optimism-corrected AUCs are obtained for nearest centroid (NC), diagonallinear discriminant analysis (DLDA), or support vector machines (SVM)using the original 200 SVM classifier (375).

In some instances, the cohort-based method comprises biomarker discoveryand validation. Transplant recipients with known conditions (e.g. AR,ADNR, CAN, SCAR, TX) are randomly split into a discovery cohort and avalidation cohort. One or more gene expression products may be measuredfor all the subjects in both cohorts. In some instances, at least 5, 10,15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100,110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400,450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1500, 2000,2500 or more gene expression products are measured for all the subjects.In some instances, the gene expression products with differentconditions (e.g. AR, ADNR, CAN, SCAR, TX) in the discovery cohort arecompared and differentially expressed probe sets are discovered asbiomarkers. For example, the discovery cohort in FIG. 3 yielded 2977differentially expressed probe sets (Table 1). In some instances, thedifference in gene expression level is at least 10%, 15%, 20%, 25%, 30%,35%, 40%, 45% or 50% or more. In some instances, the difference in geneexpression level is at least 2, 3, 4, 5, 6, 7, 8, 9, 10 fold or more. Insome instances, the accuracy is calculated using a trained algorithm.For example, the present invention may provide gene expression productscorresponding to genes selected from Table 1a. The present invention mayalso provide gene expression products corresponding to genes selectedfrom Table 1c. In some instances, the accuracy is calculated using atrained algorithm. For example, the present invention may provide geneexpression products corresponding to genes selected from Table 1a, 1b,1c, or 1d, in any combination. In some instances, the accuracy iscalculated using a trained algorithm. For example, the present inventionmay provide gene expression products corresponding to genes selectedfrom Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in anycombination. In some instances, the identified probe sets may be used totrain an algorithm for purposes of identification, diagnosis,classification, treatment or to otherwise characterize variousconditions (e.g. AR, ADNR, CAN, SCAR, TX) of organ transplant.

The differentially expressed probe sets and/or algorithm may be subjectto validation. In some instances, classification of the transplantcondition may be made by applying the probe sets and/or algorithmgenerated from the discovery cohort to the gene expression products inthe validation cohort. In some instances, the classification may bevalidated by the known condition of the subject. For example, in someinstances, the subject is identified with a particular condition (e.g.AR, ADNR, CAN, SCAR, TX) with an accuracy of greater than 60%, 65%, 70%,75%, 80%, 85%, 90%, 95%, 99% or more. In some instances, the subject isidentified with a particular condition (e.g. AR, ADNR, CAN, SCAR, TX)with a sensitivity of greater than 60%, 65%, 70%, 75%, 80%, 85%, 90%,95%, 99% or more. In some instances, the subject is identified with aparticular condition (e.g. AR, ADNR, CAN, SCAR, TX) with a specificityof greater than 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99% or more. Insome instances, biomarkers and/or algorithms may be used inidentification, diagnosis, classification and/or prediction of thetransplant condition of a subject. For example, biomarkers and/oralgorithms may be used in classification of transplant conditions for anorgan transplant patient, whose condition may be unknown.

Biomarkers that have been validated and/or algorithms may be used inidentification, diagnosis, classification and/or prediction oftransplant conditions of subjects. In some instances, gene expressionproducts of the organ transplant subjects may be compared with one ormore different sets of biomarkers. The gene expression products for eachset of biomarkers may comprise one or more reference gene expressionlevels. The reference gene expression levels may correlate with acondition (e.g. AR, ADNR, CAN, SCAR, TX) of an organ transplant.

The expression level may be compared to gene expression data for two ormore biomarkers in a sequential fashion. Alternatively, the expressionlevel is compared to gene expression data for two or more biomarkerssimultaneously. Comparison of expression levels to gene expression datafor sets of biomarkers may comprise the application of a classifier. Forexample, analysis of the gene expression levels may involve sequentialapplication of different classifiers described herein to the geneexpression data. Such sequential analysis may involve applying aclassifier obtained from gene expression analysis of cohorts oftransplant recipients with a first status or outcome (e.g., transplantrejection), followed by applying a classifier obtained from analysis ofa mixture of different samples, some of such samples obtained fromhealthy transplant recipients, transplant recipients experiencingtransplant rejection, and/or transplant recipients experiencing organdysfunction with no transplant rejection. Alternatively, sequentialanalysis involves applying at least two different classifiers obtainedfrom gene expression analysis of transplant recipients, wherein at leastone of the classifiers correlates to transplant dysfunction with norejection.

Classifiers and Classifier Probe Sets

Disclosed herein is the use of a classification system comprises one ormore classifiers. In some instances, the classifier is a 2-, 3-, 4-, 5-,6-, 7-, 8-, 9-, or 10-way classifier. In some instances, the classifieris a 15-, 20-, 25-, 30-, 35-, 40-, 45-, 50-, 55-, 60-, 65-, 70-, 75-,80-, 85-, 90-, 95-, or 100-way classifier. In some preferredembodiments, the classifier is a three-way classifier. In someembodiments, the classifier is a four-way classifier.

A two-way classifier may classify a sample from a subject into one oftwo classes. In some instances, a two-way classifier may classify asample from an organ transplant recipient into one of two classescomprising acute rejection (AR) and normal transplant function (TX). Insome instances, a two-way classifier may classify a sample from an organtransplant recipient into one of two classes comprising acute rejection(AR) and acute dysfunction with no rejection (ADNR). In some instances,a two-way classifier may classify a sample from an organ transplantrecipient into one of two classes comprising normal transplant function(TX) and acute dysfunction with no rejection (ADNR). In some instances,a three-way classifier may classify a sample from a subject into one ofthree classes. A three-way classifier may classify a sample from anorgan transplant recipient into one of three classes comprising acuterejection (AR), acute dysfunction with no rejection (ADNR) and normaltransplant function (TX). In some instances, a three-way classifier maya sample from an organ transplant recipient into one of three classeswherein the classes can include a combination of any one of acuterejection (AR), acute dysfunction with no rejection (ADNR), normaltransplant function (TX), chronic allograft nephropathy (CAN),interstitial fibrosis and/or tubular atrophy (IF/TA), or SubclinicalAcute Rejection (SCAR). In some cases, the three-way classifier mayclassify a sample as AR/HCV-R/Tx. In some cases, the classifier is afour-way classifier. In some cases, the four-way classifier may classifya sample as AR, HCV-R, AR+HCV, or TX.

Classifiers and/or classifier probe sets may be used to either rule-inor rule-out a sample as healthy. For example, a classifier may be usedto classify a sample as being from a healthy subject. Alternatively, aclassifier may be used to classify a sample as being from an unhealthysubject. Alternatively, or additionally, classifiers may be used toeither rule-in or rule-out a sample as transplant rejection. Forexample, a classifier may be used to classify a sample as being from asubject suffering from a transplant rejection. In another example, aclassifier may be used to classify a sample as being from a subject thatis not suffering from a transplant rejection. Classifiers may be used toeither rule-in or rule-out a sample as transplant dysfunction with norejection. For example, a classifier may be used to classify a sample asbeing from a subject suffering from transplant dysfunction with norejection. In another example, a classifier may be used to classify asample as not being from a subject suffering from transplant dysfunctionwith no rejection.

Classifiers used in sequential analysis may be used to either rule-in orrule-out a sample as healthy, transplant rejection, or transplantdysfunction with no rejection. For example, a classifier may be used toclassify a sample as being from an unhealthy subject. Sequentialanalysis with a classifier may further be used to classify the sample asbeing from a subject suffering from a transplant rejection. Sequentialanalysis may end with the application of a “main” classifier to datafrom samples that have not been ruled out by the preceding classifiers.For example, classifiers may be used in sequential analysis of tensamples. The classifier may classify 6 out of the 10 samples as beingfrom healthy subjects and 4 out of the 10 samples as being fromunhealthy subjects. The 4 samples that were classified as being fromunhealthy subjects may be further analyzed with the classifiers.Analysis of the 4 samples may determine that 3 of the 4 samples are fromsubjects suffering from a transplant rejection. Further analysis may beperformed on the remaining sample that was not classified as being froma subject suffering from a transplant rejection. The classifier may beobtained from data analysis of gene expression levels in multiple typesof samples. The classifier may be capable of designating a sample ashealthy, transplant rejection or transplant dysfunction with norejection.

Classifier probe sets, classification systems and/or classifiersdisclosed herein may be used to either classify (e.g., rule-in orrule-out) a sample as healthy or unhealthy. Sample classification maycomprise the use of one or more additional classifier probe sets,classification systems and/or classifiers to further analyze theunhealthy samples. Further analysis of the unhealthy samples maycomprise use of the one or more additional classifier probe sets,classification systems and/or classifiers to either classify (e.g.,rule-in or rule-out) the unhealthy sample as transplant rejection ortransplant dysfunction with no rejection. Sample classification may endwith the application of a classifier probe set, classification systemand/or classifier to data from samples that have not been ruled out bythe preceding classifier probe sets, classification systems and/orclassifiers. The classifier probe set, classification system and/orclassifier may be obtained from data analysis of gene expression levelsin multiple types of samples. The classifier probe set, classificationsystem and/or classifier may be capable of designating a sample ashealthy, transplant rejection or transplant dysfunction which mayinclude transplant dysfunction with no rejection. Alternatively, theclassifier probe set, classification system and/or classifier is capableof designating an unhealthy sample as transplant rejection or transplantdysfunction with no rejection.

The differentially expressed genes may be genes that may bedifferentially expressed in a plurality of control samples. For example,the plurality of control samples may comprise two or more samples thatmay be differentially classified as acute rejection, acute dysfunctionno rejection or normal transplant function. The plurality of controlsamples may comprise three or more samples that may be differentiallyclassified. The samples may be differentially classified based on one ormore clinical features. The one or more clinical features may comprisestatus or outcome of a transplanted organ. The one or more clinicalfeatures may comprise diagnosis of transplant rejection. The one or moreclinical features may comprise diagnosis of transplant dysfunction. Theone or more clinical features may comprise one or more symptoms of thesubject from which the sample is obtained from. The one or more clinicalfeatures may comprise age and/or gender of the subject from which thesample is obtained from. The one or more clinical features may compriseresponse to one or more immunosuppressive regimens. The one or moreclinical features may comprise a number of immunosuppressive regimens.

The classifier set may comprise one or more genes that may bedifferentially expressed in two or more control samples. The two or morecontrol samples may be differentially classified. The two or morecontrol samples may be differentially classified as acute rejection,acute dysfunction no rejection or normal transplant function. Theclassifier set may comprise one or more genes that may be differentiallyexpressed in three or more control samples. The three or more controlsamples may be differentially classified.

The method of producing a classifier set may comprise comparing two ormore gene expression profiles from two or more control samples. The twoor more gene expression profiles from the two or more control samplesmay be normalized. The two or more gene expression profiles may benormalized by different tools including use of frozen robust multichipaverage (fRMA). In some instances, the two or more gene expressionprofiles are not normalized by quantile normalization.

The method of producing a classifier set may comprise applying analgorithm to two or more expression profiles from two or more controlsamples. The classifier set may comprise one or more genes selected byapplication of the algorithm to the two or more expression profiles. Themethod of producing the classifier set may further comprise generating ashrunken centroid parameter for the one or more genes in the classifierset.

The classifier set may be generated by statistical bootstrapping.Statistical bootstrapping may comprise creating multiple computationalpermutations and cross validations using a control sample set.

Disclosed herein is the use of a classifier probe set for determining anexpression level of one or more genes in preparation of a kit forclassifying a sample from a subject, wherein the classifier probe set isbased on a classification system comprising three or more classes. Atleast two of the classes may be selected from transplant rejection,transplant dysfunction with no rejection and normal transplant function.All three classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function.

Further disclosed herein is a classifier probe set for use inclassifying a sample from a subject, wherein the classifier probe set isbased on a classification system comprising three or more classes. Atleast two of the classes may be selected from transplant rejection,transplant dysfunction with no rejection and normal transplant function.All three classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function.

Further disclosed herein is the use of a classification systemcomprising three or more classes in preparation of a probe set forclassifying a sample from a subject. At least two of the classes may beselected from transplant rejection, transplant dysfunction with norejection and normal transplant function. At least three of the three ormore classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. Often, theclasses are different classes.

Further disclosed herein are classification systems for classifying oneor more samples from one or more subjects. The classification system maycomprise three or more classes. At least two of the classes may beselected from transplant rejection, transplant dysfunction with norejection and normal transplant function. All three classes may beselected from transplant rejection, transplant dysfunction with norejection and normal transplant function.

Classifiers may comprise panels of biomarkers. Expression profilingbased on panels of biomarkers may be used to characterize a sample ashealthy, transplant rejection and/or transplant dysfunction with norejection. Panels may be derived from analysis of gene expression levelsof cohorts containing healthy transplant recipients, transplantrecipients experiencing transplant rejection and/or transplantrecipients experiencing transplant dysfunction with no rejection. Panelsmay be derived from analysis of gene expression levels of cohortscontaining transplant recipients experiencing transplant dysfunctionwith no rejection. Exemplary panels of biomarkers can be derived fromgenes listed in Table 1a. Exemplary panels of biomarkers can also bederived from genes listed in Table 1c. Exemplary panels of biomarkerscan be derived from genes listed in Table 1a, 1b, 1c, or 1d, in anycombination. Exemplary panels of biomarkers can be derived from geneslisted in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b,in any combination.

Sample Cohorts

In some embodiments, the methods, kits and systems of the presentinvention seek to improve upon the accuracy of current methods ofclassifying samples obtained from transplant recipients. In someembodiments, the methods provide improved accuracy of identifyingsamples as normal function (e.g., healthy), transplant rejection ortransplant dysfunction with no rejection. In some embodiments, themethods provide improved accuracy of identifying samples as normalfunction (e.g., healthy), AR or ADNR. Improved accuracy may be obtainedby using algorithms trained with specific sample cohorts, high numbersof samples, samples from individuals located in diverse geographicalregions, samples from individuals with diverse ethnic backgrounds,samples from individuals with different genders, and/or samples fromindividuals from different age groups.

The sample cohorts may be from female, male or a combination thereof. Insome cases, the sample cohorts are from at least about 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, or 80 or more different geographical locations. The geographicallocations may comprise sites spread out across a nation, a continent, orthe world. Geographical locations include, but are not limited to, testcenters, medical facilities, medical offices, hospitals, post officeaddresses, zip codes, cities, counties, states, nations, and continents.In some embodiments, a classifier that is trained using sample cohortsfrom the United States may need to be retrained for use on samplecohorts from other geographical regions (e.g., Japan, China, Europe,etc.). In some cases, the sample cohorts are from at least about 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20 or more differentethnic groups. In some embodiments, a classifier that is trained usingsample cohorts from a specific ethnic group may need to be retrained foruse on sample cohorts from other ethnic groups. In some cases, thesample cohorts are from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ormore different age groups. The age groups may be grouped into 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, or 30 or more years, or a combination thereof.Age groups may include, but are not limited to, under 10 years old,10-15 years old, 15-20 years old, 20-25 years old, 25-30 years old,30-35 years old, 35-40 years old, 40-45 years old, 45-50 years old,50-55 years old, 55-60 years old, 60-65 years old, 65-70 years old,70-75 years old, 75-80 years old, and over 80 years old. In someembodiments, a classifier that is trained using sample cohorts from aspecific age group (e.g., 30-40 years old) may need to be retrained foruse on sample cohorts from other age groups (e.g., 20-30 years old,etc.).

Methods of Classifying Samples

The samples may be classified simultaneously. The samples may beclassified sequentially. The two or more samples may be classified attwo or more time points. The samples may be obtained at 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more timepoints. The samples may be obtained at 10, 20, 30, 40, 50, 60, 70, 80,90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more timepoints. The samples may be obtained at 100, 200, 300, 400, 500, 600,700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,1900, 2000 or more time points. The two or more time points may be 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreminutes apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more hours apart.The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20 or more days apart. The two or moretime points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20 or more weeks apart. The two or more time points maybe 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20or more months apart. The two or more time points may be 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more yearsapart. The two or more time points may be at least about 6 hours apart.The two or more time points may be at least about 12 hours apart. Thetwo or more time points may be at least about 24 hours apart. The two ormore time points may be at least about 2 days apart. The two or moretime points may be at least about 1 week apart. The two or more timepoints may be at least about 1 month apart. The two or more time pointsmay be at least about 3 months apart. The two or more time points may beat least about 6 months apart. The three or more time points may be atthe same interval. For example, the first and second time points may be1 month apart and the second and third time points may be 1 month apart.The three or more time points may be at different intervals. Forexample, the first and second time points may be 1 month apart and thesecond and third time points may be 3 months apart.

Methods of simultaneous classifier-based analysis of one or more samplesmay comprise applying one or more algorithm to data from one or moresamples to simultaneously produce one or more lists, wherein the listscomprise one or more samples classified as being from healthy subjects(e.g. subjects with a normal functioning transplant (TX)), unhealthysubjects, subjects suffering from transplant rejection, subjectssuffering from transplant dysfunction, subjects suffering from acuterejection (AR), subjects suffering from acute dysfunction with norejection (ADNR), subjects suffering from chronic allograft nephropathy(CAN), subjects suffering from interstitial fibrosis and/or tubularatrophy (IF/TA), and/or subjects suffering from subclinical acuterejection (SCAR).

Methods of sequential classifier-based analysis of one or more samplesmay comprise (a) applying a first algorithm to data from one or moresamples to produce a first list; and (b) applying a second algorithm todata from the one or more samples that were excluded from the first listto produce a second list. The first list or the second list may compriseone or more samples classified as being from healthy subjects (e.g.subjects with a normal functioning transplant (TX)). The first list orthe second list may comprise one or more samples classified as beingfrom unhealthy subjects. The first list or the second list may compriseone or more samples classified as being from subjects suffering fromtransplant rejection. The first list or the second list may comprise oneor more samples classified as being from subjects suffering fromtransplant dysfunction. The first list or the second list may compriseone or more samples classified as being from subjects suffering fromacute rejection (AR). The first list or the second list may comprise oneor more samples classified as being from subjects suffering from acutedysfunction with no rejection (ADNR). The first list or the second listmay comprise one or more samples classified as being from subjectssuffering from chronic allograft nephropathy (CAN). The first list orthe second list may comprise one or more samples classified as beingfrom subjects suffering from interstitial fibrosis and/or tubularatrophy (IF/TA). The first list or the second list may comprise one ormore samples classified as being from subjects suffering fromsubclinical acute rejection (SCAR). For example, a sequentialclassifier-based analysis may comprise (a) applying a first algorithm todata from one or more samples to produce a first list, wherein the firstlist comprises one or more samples classified as being from healthysubjects; and (b) applying a second algorithm to data from the one ormore samples that were excluded from the first list to produce a secondlist, wherein the second list comprises one or more samples classifiedas being from subjects suffering from transplant rejection.

The methods may undergo further iteration. One or more additional listsmay be produced by applying one or more additional algorithms. The firstalgorithm, second algorithm, and/or one or more additional algorithmsmay be the same. The first algorithm, second algorithm, and/or one ormore additional algorithms may be different. In some instances, the oneor more additional lists may be produced by applying one or moreadditional algorithms to data from one or more samples from one or moreprevious lists. The one or more additional lists may comprise one ormore samples classified as being from healthy subjects (e.g. subjectswith a normal functioning transplant (TX)). The one or more additionallists may comprise one or more samples classified as being fromunhealthy subjects. The one or more additional lists may comprise one ormore samples classified as being from subjects suffering from transplantrejection. The one or more additional lists may comprise one or moresamples classified as being from subjects suffering from transplantdysfunction. The one or more additional lists may comprise one or moresamples classified as being from subjects suffering from acute rejection(AR). The one or more additional lists may comprise one or more samplesclassified as being from subjects suffering from acute dysfunction withno rejection (ADNR). The one or more additional lists may comprise oneor more samples classified as being from subjects suffering from chronicallograft nephropathy (CAN). The one or more additional lists maycomprise one or more samples classified as being from subjects sufferingfrom interstitial fibrosis and/or tubular atrophy (IF/TA). The one ormore additional lists may comprise one or more samples classified asbeing from subjects suffering from subclinical acute rejection (SCAR).

This disclosure also provides one or more steps or analyses that may beused in addition to applying a classifier or algorithm to expressionlevel data from a sample, such as a clinical sample. Such series ofsteps may include, but are not limited to, initial cytology orhistopathology study of the sample, followed by analysis of gene (orother biomarker) expression levels in the sample. In some embodiments,the one or more steps or analyses (e.g., cytology or histopathologystudy) occur prior to the step of applying any of the classifier probesets or classification systems described herein. The one or more stepsor analyses (e.g., cytology or histopathology study) may occurconcurrently with the step of applying any of the classifier probe setsor classification systems described herein. Alternatively, the one ormore steps or analyses (e.g., cytology or histopathology study) mayoccur after the step of applying any of the classifier probe sets orclassification systems described herein.

Sequential classifier-based analysis of the samples may occur in variousorders. For example, sequential classifier-based analysis of one or moresamples may comprise classifying samples as healthy or unhealthy,followed by classification of unhealthy samples as transplant rejectionor non-transplant rejection, followed by classification ofnon-transplant rejection samples as transplant dysfunction or transplantdysfunction with no rejection. In another example, sequentialclassifier-based analysis of one or more samples may compriseclassifying samples as transplant dysfunction or no transplantdysfunction, followed by classification of transplant dysfunctionsamples as transplant rejection or no transplant rejection. The notransplant dysfunction samples may further be classified as healthy. Inanother example, sequential classifier-based analysis comprisesclassifying samples as transplant rejection or no transplant rejection,followed by classification of the no transplant rejection samples ashealthy or unhealthy. The unhealthy samples may be further classified astransplant dysfunction or no transplant dysfunction. Sequentialclassifier-based analysis may comprise classifying samples as transplantrejection or no transplant rejection, followed by classification of theno transplant rejection samples as transplant dysfunction or notransplant dysfunction. The no transplant dysfunction samples mayfurther be classified as healthy or unhealthy. The unhealthy samples mayfurther be classified as transplant rejection or no transplantrejection. The unhealthy samples may further be classified as chronicallograft nephropathy/interstitial fibrosis and tubular atrophy(CAN/IFTA) or no CAN/IFTA. The unhealthy samples may further beclassified as transplant dysfunction or no transplant dysfunction. Thetransplant dysfunction samples may be further classified as transplantdysfunction with no rejection or transplant dysfunction with rejection.The transplant dysfunction samples may be further classified astransplant rejection or no transplant rejection. The transplantrejection samples may further be classified as chronic allograftnephropathy/interstitial fibrosis and tubular atrophy (CAN/IFTA) or noCAN/IFTA.

Algorithms

The methods, kits, and systems disclosed herein may comprise one or morealgorithms or uses thereof. The one or more algorithms may be used toclassify one or more samples from one or more subjects. The one or morealgorithms may be applied to data from one or more samples. The data maycomprise gene expression data. The data may comprise sequencing data.The data may comprise array hybridization data.

The methods disclosed herein may comprise assigning a classification toone or more samples from one or more subjects. Assigning theclassification to the sample may comprise applying an algorithm to theexpression level. In some cases, the gene expression levels are inputtedto a trained algorithm for classifying the sample as one of theconditions comprising AR, ADNR, or TX.

The algorithm may provide a record of its output including aclassification of a sample and/or a confidence level. In some instances,the output of the algorithm can be the possibility of the subject ofhaving a condition, such as AR, ADNR, or TX. In some instances, theoutput of the algorithm can be the risk of the subject of having acondition, such as AR, ADNR, or TX. In some instances, the output of thealgorithm can be the possibility of the subject of developing into acondition in the future, such as AR, ADNR, or TX.

The algorithm may be a trained algorithm. The algorithm may comprise alinear classifier. The linear classifier may comprise one or more lineardiscriminant analysis, Fisher's linear discriminant, Naïve Bayesclassifier, Logistic regression, Perceptron, Support vector machine, ora combination thereof. The linear classifier may be a Support vectormachine (SVM) algorithm.

The algorithm may comprise one or more linear discriminant analysis(LDA), Basic perceptron, Elastic Net, logistic regression, (Kernel)Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis(DLDA), Golub Classifier, Parzen-based, (kernel) Fisher DiscriminantClassifier, k-nearest neighbor, Iterative RELIEF, Classification Tree,Maximum Likelihood Classifier, Random Forest, Nearest Centroid,Prediction Analysis of Microarrays (PAM), k-medians clustering, FuzzyC-Means Clustering, Gaussian mixture models, or a combination thereof.The algorithm may comprise a Diagonal Linear Discriminant Analysis(DLDA) algorithm. The algorithm may comprise a Nearest Centroidalgorithm. The algorithm may comprise a Random Forest algorithm. Thealgorithm may comprise a Prediction Analysis of Microarrays (PAM)algorithm.

The methods disclosed herein may comprise use of one or more classifierequations. Classifying the sample may comprise a classifier equation.The classifier equation may be Equation 1:

${{\delta_{k}\left( x^{*} \right)} = {{\sum\limits_{i = 1}^{p}\frac{\left( {x_{i}^{*} - {\overset{\_}{x}}_{ik}^{\prime}} \right)^{2}}{\left( {s_{i} + s_{0}} \right)^{2}}} - {2\log \; \pi_{k}}}},$

wherein:

k is a number of possible classes;

δ_(k) may be the discriminant score for class k;

x_(i)* represents the expression level of gene i;

x* represents a vector of expression levels for all p genes to be usedfor classification drawn from the sample to be classified;

x _(k)′ may be a shrunken centroid calculated from a training data and ashrinkage factor;

x _(ik)′: may be a component of x _(k)′ corresponding to gene i;

s_(i) is a pooled within-class standard deviation for gene i in thetraining data;

s₀ is a specified positive constant; and

π_(k) represents a prior probability of a sample belonging to class k.

Assigning the classification may comprise calculating a classprobability. Calculating the class probability {circumflex over(p)}_(k)(x*) may be calculated by Equation 2:

${{\hat{p}}_{k}\left( x^{*} \right)} = {\frac{e^{{- \frac{1}{2}}{\delta_{k}{(x^{*})}}}}{\sum\limits_{l = 1}^{K}e^{{- \frac{1}{2}}{\delta_{l}{(x^{*})}}}}.}$

Assigning the classification may comprise a classification rule. Theclassification rule C(x*) may be expressed by Equation 3:

${C\left( x^{*} \right)} = {\underset{k \in {\{{1,K}\}}}{\arg \; \max}\; {{{\hat{p}}_{k}\left( x^{*} \right)}.}}$

Classification of Samples

The classifiers disclosed herein may be used to classify one or moresamples. The classifiers disclosed herein may be used to classify 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moresamples. The classifiers disclosed herein may be used to classify 10,20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170,180, 190, 200 or more samples. The classifiers disclosed herein may beused to classify 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000,1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or moresamples. The classifiers disclosed herein may be used to classify 1000,2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000,13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more samples.The classifiers disclosed herein may be used to classify 10000, 20000,30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000,130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000 or moresamples. The classifiers disclosed herein may be used to classify atleast about 5 samples. The classifiers disclosed herein may be used toclassify at least about 10 samples. The classifiers disclosed herein maybe used to classify at least about 20 samples. The classifiers disclosedherein may be used to classify at least about 30 samples. Theclassifiers disclosed herein may be used to classify at least about 50samples. The classifiers disclosed herein may be used to classify atleast about 100 samples. The classifiers disclosed herein may be used toclassify at least about 200 samples.

Two or more samples may be from the same subject. The samples may befrom two or more different subjects. The samples may be from 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moresubjects. The samples may be from 10, 20, 30, 40, 50, 60, 70, 80, 90,100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more subjects.The samples may be from 100, 200, 300, 400, 500, 600, 700, 800, 900,1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or moresubjects. The samples may be from 1000, 2000, 3000, 4000, 5000, 6000,7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000,17000, 18000, 19000, 20000 or more subjects. The samples may be from1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000,12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or moresubjects. The samples may be from 2 or more subjects. The samples may befrom 5 or more subjects. The samples may be from 10 or more subjects.The samples may be from 20 or more subjects. The samples may be from 50or more subjects. The samples may be from 70 or more subjects. Thesamples may be from 80 or more subjects. The samples may be from 100 ormore subjects. The samples may be from 200 or more subjects. The samplesmay be from 300 or more subjects. The samples may be from 500 or moresubjects.

The two or more samples may be obtained at the same time point. The twoor more samples may be obtained at two or more different time points.The samples may be obtained at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20 or more time points. The samples may beobtained at 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,150, 160, 170, 180, 190, 200 or more time points. The samples may beobtained at 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100,1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more timepoints. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more minutes apart. Thetwo or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20 or more hours apart. The two or more timepoints may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20 or more days apart. The two or more time points may be 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreweeks apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more months apart. Thetwo or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20 or more years apart. The two or more timepoints may be at least about 6 hours apart. The two or more time pointsmay be at least about 12 hours apart. The two or more time points may beat least about 24 hours apart. The two or more time points may be atleast about 2 days apart. The two or more time points may be at leastabout 1 week apart. The two or more time points may be at least about 1month apart. The two or more time points may be at least about 3 monthsapart. The two or more time points may be at least about 6 months apart.The three or more time points may be at the same interval. For example,the first and second time points may be 1 month apart and the second andthird time points may be 1 month apart. The three or more time pointsmay be at different intervals. For example, the first and second timepoints may be 1 month apart and the second and third time points may be3 months apart.

Further disclosed herein are methods of classifying one or more samplesfrom one or more subjects. The method of classifying one or more samplesfrom one or more subjects may comprise (a) obtaining an expression levelof one or more gene expression products of a sample from a subject; and(b) identifying the sample as normal transplant function if the geneexpression level indicates a lack of transplant rejection and/ortransplant dysfunction. The subject may be a transplant recipient. Thesubject may be a transplant donor. The subject may be a healthy subject.The subject may be an unhealthy subject. The method may comprisedetermining an expression level of one or more gene expression productsin one or more samples from one or more subjects. The one or moresubjects may be transplant recipients, transplant donors, or combinationthereof. The one or more subjects may be healthy subjects, unhealthysubjects, or a combination thereof. The method may further compriseidentifying the sample as transplant dysfunction if the gene expressionlevel indicates transplant rejection and/or transplant dysfunction. Themethod may further comprise identifying the sample as transplantdysfunction with no rejection if the gene expression level indicatestransplant dysfunction and a lack transplant rejection. The method mayfurther comprise identifying the sample as transplant rejection if thegene expression level indicates transplant rejection and/or transplantdysfunction. The expression level may be obtained by sequencing. Theexpression level may be obtained by RNA-sequencing. The expression levelmay be obtained by array. The array may be a microarray. The microarraymay be a peg array. The peg array may be a Gene 1.1^(ST) peg array. Thepeg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HTHG-U133+ PM Array. The sample may be a blood sample. The sample maycomprise one or more peripheral blood lymphocytes. The blood sample maybe a peripheral blood sample. The sample may be a serum sample. Thesample may be a plasma sample. The expression level may be based ondetecting and/or measuring one or more RNA. Identifying the sample maycomprise use of one or more classifier probe sets. Identifying thesample may comprise use of one or more algorithms. Identifying thesample may comprise use of one or more classification systems. Theclassification system may comprise a three-way classification. Thethree-way classification may comprise normal transplant function,transplant dysfunction with no rejection, transplant rejection, or acombination thereof. The three-way classification may comprise normaltransplant function, transplant dysfunction with no rejection, andtransplant rejection. The method may further comprise generating one ormore reports based on the identification of the sample. The method mayfurther comprise transmitting one or more reports comprising informationpertaining to the identification of the sample to the subject or amedical representative of the subject.

The method of classifying a sample may comprise (a) obtaining anexpression level of one or more gene expression products of a samplefrom a subject; and (b) identifying the sample as transplant rejectionif the gene expression level indicative of transplant rejection and/ortransplant dysfunction. The one or more subjects may be transplantrecipients. The subject may be a transplant recipient. The subject maybe a transplant donor. The subject may be a healthy subject. The subjectmay be an unhealthy subject. The method may comprise determining anexpression level of one or more gene expression products in one or moresamples from one or more subjects. The one or more subjects may betransplant recipients, transplant donors, or combination thereof. Theone or more subjects may be healthy subjects, unhealthy subjects, or acombination thereof. The method may further comprise identifying thesample as transplant dysfunction if the gene expression level indicatestransplant rejection and/or transplant dysfunction. The method mayfurther comprise identifying the sample as transplant dysfunction withno rejection if the gene expression level indicates transplantdysfunction and a lack of transplant rejection. The method may furthercomprise identifying the sample as normal function if the geneexpression level indicates a lacks of transplant rejection andtransplant dysfunction. The expression level may be obtained bysequencing. The expression level may be obtained by RNA-sequencing. Theexpression level may be obtained by array. The array may be amicroarray. The microarray may be a peg array. The peg array may be aGene 1.1^(ST) peg array. The peg array may be a Hu133 Plus 2.0PM pegarray. The peg array may be a HT HG-U133+ PM Array. The sample may be ablood sample. The sample may comprise one or more peripheral bloodlymphocytes. The blood sample may be a peripheral blood sample. Thesample may be a serum sample. The sample may be a plasma sample. Theexpression level may be based on detecting and/or measuring one or moreRNA. Identifying the sample may comprise use of one or more classifierprobe sets. Identifying the sample may comprise use of one or morealgorithms. Identifying the sample may comprise use of one or moreclassification systems. The classification system may comprise athree-way classification. The three-way classification may comprisenormal transplant function, transplant dysfunction with no rejection,transplant rejection, or a combination thereof. The three-wayclassification may comprise normal transplant function, transplantdysfunction with no rejection, and transplant rejection. The method mayfurther comprise generating one or more reports based on theidentification of the sample. The method may further comprisetransmitting one or more reports comprising information pertaining tothe identification of the sample to the subject or a medicalrepresentative of the subject.

The method of classifying a sample may comprise (a) obtaining anexpression level of one or more gene expression products of a samplefrom a subject; and (b) identifying the sample as transplant dysfunctionwith no rejection wherein the gene expression level indicative oftransplant dysfunction and the gene expression level indicates a lack oftransplant rejection. The subject may be a transplant recipient. Thesubject may be a transplant donor. The subject may be a healthy subject.The subject may be an unhealthy subject. The method may comprisedetermining an expression level of one or more gene expression productsin one or more samples from one or more subjects. The one or moresubjects may be transplant recipients, transplant donors, or combinationthereof. The one or more subjects may be healthy subjects, unhealthysubjects, or a combination thereof. The method may further compriseidentifying the sample as normal transplant function if the geneexpression level indicates a lack of transplant dysfunction. The methodmay further comprise identifying the sample as transplant rejection ifthe gene expression level indicates transplant rejection and/ortransplant dysfunction. The expression level may be obtained bysequencing. The expression level may be obtained by RNA-sequencing. Theexpression level may be obtained by array. The array may be amicroarray. The microarray may be a peg array. The peg array may be aGene 1.1^(ST) peg array. The peg array may be a Hu133 Plus 2.0PM pegarray. The peg array may be a HT HG-U133+ PM Array. The sample may be ablood sample. The sample may comprise one or more peripheral bloodlymphocytes. The blood sample may be a peripheral blood sample. Thesample may be a serum sample. The sample may be a plasma sample. Theexpression level may be based on detecting and/or measuring one or moreRNA. Identifying the sample may comprise use of one or more classifierprobe sets. Identifying the sample may comprise use of one or morealgorithms. Identifying the sample may comprise use of one or moreclassification systems. The classification system may comprise athree-way classification. The three-way classification may comprisenormal transplant function, transplant dysfunction with no rejection,transplant rejection, or a combination thereof. The three-wayclassification may comprise normal transplant function, transplantdysfunction with no rejection, and transplant rejection. The method mayfurther comprise generating one or more reports based on theidentification of the sample. The method may further comprisetransmitting one or more reports comprising information pertaining tothe identification of the sample to the subject or a medicalrepresentative of the subject.

The method of classifying a sample may comprise (a) determining anexpression level of one or more gene expression products in a samplefrom a subject; and (b) assigning a classification to the sample basedon the level of expression of the one or more gene products, wherein theclassification comprises transplant dysfunction with no rejection. Insome embodiments, the gene expression products are associated with oneor more biomarkers selected from gene expression products correspondingto genes listed in Table 1a. In some embodiments, the gene expressionproducts are associated with one or more biomarkers selected from geneexpression products corresponding to genes listed in Table 1c. In someembodiments, the gene expression products are associated with one ormore biomarkers selected from gene expression products corresponding togenes listed in Table 1a, 1b, 1c, or 1d, in any combination. In someembodiments, the gene expression products are associated with one ormore biomarkers selected from gene expression products corresponding togenes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or18b, in any combination. The subject may be a transplant recipient. Thesubject may be a transplant donor. The subject may be a healthy subject.The subject may be an unhealthy subject. The method may comprisedetermining an expression level of one or more gene expression productsin one or more samples from one or more subjects. The one or moresubjects may be transplant recipients, transplant donors, or combinationthereof. The one or more subjects may be healthy subjects, unhealthysubjects, or a combination thereof. The method may further compriseclassifying the sample as transplant dysfunction. The method may furthercomprise classifying the sample as transplant dysfunction with norejection. The method may further comprise classifying the sample asnormal function. The method may further comprise classifying the sampleas transplant rejection. The expression level may be obtained bysequencing. The expression level may be obtained by RNA-sequencing. Theexpression level may be obtained by array. The array may be amicroarray. The microarray may be a peg array. The peg array may be aGene 1.1^(ST) peg array. The peg array may be a Hu133 Plus 2.0PM pegarray. The peg array may be a HT HG-U133+ PM Array. The sample may be ablood sample. The sample may comprise one or more peripheral bloodlymphocytes. The blood sample may be a peripheral blood sample. Thesample may be a serum sample. The sample may be a plasma sample. Theexpression level may be based on detecting and/or measuring one or moreRNA. Classifying the sample may comprise use of one or more classifierprobe sets. Classifying the sample may comprise use of one or morealgorithms. The classification system may further comprise normaltransplant function. The classification system may further comprisetransplant rejection. The classification system may further compriseCAN. The classification system may further comprise IF/TA. Theclassification system may comprise a three-way classification. Thethree-way classification may comprise normal transplant function,transplant dysfunction with no rejection, transplant rejection, or acombination thereof. The three-way classification may comprise normaltransplant function, transplant dysfunction with no rejection, andtransplant rejection. The method may further comprise generating one ormore reports based on the identification of the sample. The method mayfurther comprise transmitting one or more reports comprising informationpertaining to the identification of the sample to the subject or amedical representative of the subject.

The method of classifying a sample may comprise (a) determining anexpression level of one or more gene expression products in a samplefrom a subject; and (b) assigning a classification to the sample basedon the level of expression of the one or more gene products, wherein theclassification comprises transplant rejection, transplant dysfunctionwith no rejection and normal transplant function. In some embodiments,the gene expression products are associated with one or more biomarkersselected from gene expression products corresponding to genes listed inTable 1a. In some embodiments, the gene expression products areassociated with one or more biomarkers selected from gene expressionproducts corresponding to genes listed in Table 1c. In some embodiments,the gene expression products are associated with one or more biomarkersselected from gene expression products corresponding to genes listed inTable 1a, 1b, 1c, or 1d, in any combination. In some embodiments, thegene expression products are associated with one or more biomarkersselected from gene expression products corresponding to genes listed inTable 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in anycombination. The subject may be a transplant recipient. The subject maybe a transplant donor. The subject may be a healthy subject. The subjectmay be an unhealthy subject. The method may comprise determining anexpression level of one or more gene expression products in one or moresamples from one or more subjects. The one or more subjects may betransplant recipients, transplant donors, or combination thereof. Theone or more subjects may be healthy subjects, unhealthy subjects, or acombination thereof. The method may further comprise classifying thesample as transplant dysfunction. The method may further compriseclassifying the sample as transplant dysfunction with no rejection. Themethod may further comprise classifying the sample as normal function.The method may further comprise classifying the sample as transplantrejection. The expression level may be obtained by sequencing. Theexpression level may be obtained by RNA-sequencing. The expression levelmay be obtained by array. The array may be a microarray. The microarraymay be a peg array. The peg array may be a Gene 1.1^(ST) peg array. Thepeg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HTHG-U133+ PM Array. The sample may be a blood sample. The sample maycomprise one or more peripheral blood lymphocytes. The blood sample maybe a peripheral blood sample. The sample may be a serum sample. Thesample may be a plasma sample. The expression level may be based ondetecting and/or measuring one or more RNA. Classifying the sample maycomprise use of one or more classifier probe sets. Classifying thesample may comprise use of one or more algorithms. The classificationsystem may further comprise CAN. The classification system may furthercomprise IF/TA. The method may further comprise generating one or morereports based on the identification of the sample. The method mayfurther comprise transmitting one or more reports comprising informationpertaining to the identification of the sample to the subject or amedical representative of the subject.

The method of classifying a sample may comprise (a) determining a levelof expression of a plurality of genes in a sample from a subject; and(b) classifying the sample by applying an algorithm to the expressionlevel data from step (a), wherein the algorithm is not validated by acohort-based analysis of an entire cohort. In some embodiments, theplurality of genes is associated with one or more biomarkers selectedfrom gene expression products corresponding to genes listed in Table 1a.In some embodiments, the plurality of genes is associated with one ormore biomarkers selected from gene expression products corresponding togenes listed in Table 1c. In some embodiments, the plurality of genes isassociated with one or more biomarkers selected from gene expressionproducts corresponding to genes listed in Table 1a, 1b, 1c, or 1d, inany combination. In some embodiments, the plurality of genes isassociated with one or more biomarkers selected from gene expressionproducts corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9,10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may bea transplant recipient. The subject may be a transplant donor. Thesubject may be a healthy subject. The subject may be an unhealthysubject. The method may comprise determining an expression level of oneor more gene expression products in one or more samples from one or moresubjects. The one or more subjects may be transplant recipients,transplant donors, or combination thereof. The one or more subjects maybe healthy subjects, unhealthy subjects, or a combination thereof. Themethod may further comprise classifying the sample as transplantdysfunction. The method may further comprise classifying the sample astransplant dysfunction with no rejection. The method may furthercomprise classifying the sample as normal function. The method mayfurther comprise classifying the sample as transplant rejection. Theexpression level may be obtained by sequencing. The expression level maybe obtained by RNA-sequencing. The expression level may be obtained byarray. The array may be a microarray. The microarray may be a peg array.The peg array may be a Gene 1.1^(ST) peg array. The peg array may be aHu133 Plus 2.0PM peg array. The sample may be a blood sample. The samplemay comprise one or more peripheral blood lymphocytes. The blood samplemay be a peripheral blood sample. The sample may be a serum sample. Thesample may be a plasma sample. The expression level may be based ondetecting and/or measuring one or more RNA. Classifying the sample maycomprise use of one or more classifier probe sets. Classifying thesample may comprise use of one or more algorithms. Classifying thesample may comprise use of a classification system. The classificationsystem may further comprise normal transplant function. Theclassification system may further comprise transplant rejection. Theclassification system may further comprise CAN. The classificationsystem may further comprise IF/TA. The classification system maycomprise a three-way classification. The three-way classification maycomprise normal transplant function, transplant dysfunction with norejection, transplant rejection, or a combination thereof. The three-wayclassification may comprise normal transplant function, transplantdysfunction with no rejection, and transplant rejection. The method mayfurther comprise generating one or more reports based on theidentification of the sample. The method may further comprisetransmitting one or more reports comprising information pertaining tothe identification of the sample to the subject or a medicalrepresentative of the subject. The algorithm may be validated byanalysis of less than or equal to about 97%, 95%, 93%, 90%, 87%, 85%,83%, 80%, 77%, 75%, 73%, 70%, 67%, 65%, 53%, 60%, 57%, 55%, 53%, 50%,47%, 45%, 43%, 40%, 37%, 35%, 33%, 30%, 27%, 25%, 23%, 20%, 17%, 15%,13%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, or 3% of the entire cohort. Thealgorithm may be validated by analysis of less than or equal to about70% of the entire cohort. The algorithm may be validated by analysis ofless than or equal to about 60% of the entire cohort. The algorithm maybe validated by analysis of less than or equal to about 50% of theentire cohort. The algorithm may be validated by analysis of less thanor equal to about 40% of the entire cohort.

The method of classifying a sample may comprise (a) determining a levelof expression of a plurality of genes in a sample from a subject; and(b) classifying the sample by applying an algorithm to the expressionlevel data from step (a), wherein the algorithm is validated by acombined analysis of expression level data from a plurality of samples,wherein the plurality of samples comprises at least one sample with anunknown phenotype and at least one sample with a known phenotype. Insome embodiments, the plurality of genes is associated with one or morebiomarkers selected from gene expression products corresponding to geneslisted in Table 1a. In some embodiments, the plurality of genes isassociated with one or more biomarkers selected from gene expressionproducts corresponding to genes listed in Table 1c. In some embodiments,the plurality of genes is associated with one or more biomarkersselected from gene expression products corresponding to genes listed inTable 1a, 1b, 1c, or 1d, in any combination. In some embodiments, theplurality of genes is associated with one or more biomarkers selectedfrom gene expression products corresponding to genes listed in Table 1a,1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.The subject may be a transplant recipient. The subject may be atransplant donor. The subject may be a healthy subject. The subject maybe an unhealthy subject. The method may comprise determining anexpression level of one or more gene expression products in one or moresamples from one or more subjects. The one or more subjects may betransplant recipients, transplant donors, or combination thereof. Theone or more subjects may be healthy subjects, unhealthy subjects, or acombination thereof. The method may further comprise classifying thesample as transplant dysfunction. The method may further compriseclassifying the sample as transplant dysfunction with no rejection. Themethod may further comprise classifying the sample as normal function.The method may further comprise classifying the sample as transplantrejection. The expression level may be obtained by sequencing. Theexpression level may be obtained by RNA-sequencing. The expression levelmay be obtained by array. The array may be a microarray. The microarraymay be a peg array. The peg array may be a Gene 1.1^(ST) peg array. Thepeg array may be a Hu133 Plus 2.0PM peg array. The sample may be a bloodsample. The sample may comprise one or more peripheral bloodlymphocytes. The blood sample may be a peripheral blood sample. Thesample may be a serum sample. The sample may be a plasma sample. Theexpression level may be based on detecting and/or measuring one or moreRNA. Classifying the sample may comprise use of one or more classifierprobe sets. Classifying the sample may comprise use of one or morealgorithms. Classifying the sample may comprise use of a classificationsystem. The classification system may further comprise normal transplantfunction. The classification system may further comprise transplantrejection. The classification system may further comprise CAN. Theclassification system may further comprise IF/TA. The classificationsystem may comprise a three-way classification. The three-wayclassification may comprise normal transplant function, transplantdysfunction with no rejection, transplant rejection, or a combinationthereof. The three-way classification may comprise normal transplantfunction, transplant dysfunction with no rejection, and transplantrejection. The method may further comprise generating one or morereports based on the identification of the sample. The method mayfurther comprise transmitting one or more reports comprising informationpertaining to the identification of the sample to the subject or amedical representative of the subject. At least about 1%, 2%, 3%, 4%,5%, 6%, 7%, 8%, 9%, 10%, 12%, 15%, 17%, 20%, 23%, 25%, 27%, 30% or moreof the samples from the plurality of samples may have an unknownphenotype. At least about 35%, 40%, 45%, 50%, 55%, 57%, 60%, 65%, 70%,75%, 80%, 85%, 90%, 95%, 97% or more of the samples from the pluralityof samples may have an unknown phenotype. At least about 1%, 2%, 3%, 4%,5%, 6%, 7%, 8%, 9%, 10%, 12%, 15%, 17%, 20%, 23%, 25%, 27%, 30% or moreof the samples from the plurality of samples may have a known phenotype.At least about 35%, 40%, 45%, 50%, 55%, 57%, 60%, 65%, 70%, 75%, 80%,85%, 90%, 95%, 97% or more of the samples from the plurality of samplesmay have a known phenotype.

The method of classifying one or more samples from one or more subjectsmay comprise (a) determining an expression level of one or more geneexpression products in a sample from a subject; and (b) assigning aclassification to the sample based on the level of expression of the oneor more gene products, wherein the classification comprises transplantrejection, transplant dysfunction with no rejection and normaltransplant function. The subject may be a transplant recipient. Thesubject may be a transplant donor. The subject may be a healthy subject.The subject may be an unhealthy subject. The method may comprisedetermining an expression level of one or more gene expression productsin one or more samples from one or more subjects. The one or moresubjects may be transplant recipients, transplant donors, or combinationthereof. The one or more subjects may be healthy subjects, unhealthysubjects, or a combination thereof. The method may further compriseclassifying the sample as transplant dysfunction. The method may furthercomprise classifying the sample as transplant dysfunction with norejection. The method may further comprise classifying the sample asnormal function. The method may further comprise classifying the sampleas transplant rejection. The expression level may be obtained bysequencing. The expression level may be obtained by RNA-sequencing. Theexpression level may be obtained by array. The array may be amicroarray. The microarray may be a peg array. The peg array may be aGene 1.1^(ST) peg array. The peg array may be a Hu133 Plus 2.0PM pegarray. The sample may be a blood sample. The sample may comprise one ormore peripheral blood lymphocytes. The blood sample may be a peripheralblood sample. The sample may be a serum sample. The sample may be aplasma sample. The expression level may be based on detecting and/ormeasuring one or more RNA. Identifying the sample may comprise use ofone or more classifier probe sets. Classifying the sample may compriseuse of one or more algorithms. The classification may further compriseCAN. The classification may further comprise IF/TA. The method mayfurther comprise generating one or more reports based on theclassification of the sample. The method may further comprisetransmitting one or more reports comprising information pertaining tothe identification of the sample to the subject or a medicalrepresentative of the subject.

Classifying the sample may be based on the expression level of 10, 20,30, 40, 50, 60, 70, 80, 90, 100 or more gene products. Classifying thesample may be based on the expression level of 100, 200, 300, 400, 500,600, 700, 800, 900, 1000 or more gene products. Classifying the samplemay be based on the expression level of 1000, 2000, 3000, 4000, 5000,6000, 7000, 8000, 9000, 10000 or more gene products. Classifying thesample may be based on the expression level of 10000, 20000, 30000,40000, 50000, 60000, 70000, 80000, 90000, 100000 or more gene products.Classifying the sample may be based on the expression level of 25 ormore gene products. Classifying the sample may be based on theexpression level of 50 or more gene products. Classifying the sample maybe based on the expression level of 100 or more gene products.Classifying the sample may be based on the expression level of 200 ormore gene products. Classifying the sample may be based on theexpression level of 300 or more gene products.

Classifying the sample may comprise statistical bootstrapping.

Clinical Applications

The methods, compositions, systems and kits provided herein can be usedto detect, diagnose, predict or monitor a condition of a transplantrecipient. In some instances, the methods, compositions, systems andkits described herein provide information to a medical practitioner thatcan be useful in making a therapeutic decision. Therapeutic decisionsmay include decisions to: continue with a particular therapy, modify aparticular therapy, alter the dosage of a particular therapy, stop orterminate a particular therapy, altering the frequency of a therapy,introduce a new therapy, introduce a new therapy to be used incombination with a current therapy, or any combination of the above. Insome cases, the methods provided herein can be applied in anexperimental setting, e.g., clinical trial. In some instances, themethods provided herein can be used to monitor a transplant recipientwho is being treated with an experimental agent such as animmunosuppressive drug or compound. In some instances, the methodsprovided herein can be useful to determine whether a subject can beadministered an experimental agent (e.g., an agonist, antagonist,peptidomimetic, protein, peptide, nucleic acid, small molecule, or otherdrug candidate) to reduce the risk of rejection. Thus, the methodsdescribed herein can be useful in determining if a subject can beeffectively treated with an experimental agent and for monitoring thesubject for risk of rejection or continued rejection of the transplant.

Additionally or alternatively, the physician can change the treatmentregime being administered to the patient. A change in treatment regimecan include administering an additional or different drug, oradministering a higher dosage or frequency of a drug already beingadministered to the patient. Many different drugs are available fortreating rejection, such as immunosuppressive drugs used to treattransplant rejection calcineurin inhibitors (e.g., cyclosporine,tacrolimus), mTOR inhibitors (e.g., sirolimus and everolimus),anti-proliferatives (e.g., azathioprine, mycophenolic acid),corticosteroids (e.g., prednisolone and hydrocortisone) and antibodies(e.g., basiliximab, daclizumab, Orthoclone, anti-thymocyte globulin andanti-lymphocyte globulin). Conversely, if the value or other designationof aggregate expression levels of a patient indicates the patient doesnot have or is at reduced risk of transplant rejection, the physicianneed not order further diagnostic procedures, particularly not invasiveones such as biopsy. Further, the physician can continue an existingtreatment regime, or even decrease the dose or frequency of anadministered drug.

In some cases, a clinical trial can be performed on a drug in similarfashion to the monitoring of an individual patient described above,except that drug is administered in parallel to a population oftransplant patients, usually in comparison with a control populationadministered a placebo.

Detecting/Diagnosing a Condition of a Transplant Recipient

The methods, compositions, systems and kits provided herein areparticularly useful for detecting or diagnosing a condition of atransplant recipient such as a condition the transplant recipient has atthe time of testing. Exemplary conditions that can be detected ordiagnosed with the present methods include organ transplant rejection,acute rejection (AR), chronic rejection, Acute Dysfunction with NoRejection (ADNR), normal transplant function (TX) and/or Sub-ClinicalAcute Rejection (SCAR). The methods provided herein are particularlyuseful for transplant recipients who have received a kidney transplant.Exemplary conditions that can be detected or diagnosed in such kidneytransplant recipients include: AR, chronic allograft nephropathy (CAN),ADNR, SCAR, IF/TA, and TX.

The diagnosis or detection of condition of a transplant recipient may beparticularly useful in limiting the number of invasive diagnosticinterventions that are administered to the patient. For example, themethods provided herein may limit or eliminate the need for a transplantrecipient (e.g., kidney transplant recipient) to receive a biopsy (e.g.,kidney biopsies) or to receive multiple biopsies. In some instances, themethods provided herein may also help interpreting a biopsy result,especially when the biopsy result is inconclusive.

In a further embodiment, the methods provided herein can be used aloneor in combination with other standard diagnosis methods currently usedto detect or diagnose a condition of a transplant recipient, such as butnot limited to results of biopsy analysis for kidney allograftrejection, results of histopathology of the biopsy sample, serumcreatinine level, creatinine clearance, ultrasound, radiological imagingresults for the kidney, urinalysis results, elevated levels ofinflammatory molecules such as neopterin, and lymphokines, elevatedplasma interleukin (IL)-1 in azathioprine-treated patients, elevatedIL-2 in cyclosporine-treated patients, elevated IL-6 in serum and urine,intrarenal expression of cytotoxic molecules (granzyme B and perforin)and immunoregulatory cytokines (IL-2, -4, -10, interferon gamma andtransforming growth factor-b1).

The methods provided herein are useful for distinguishing between two ormore conditions or disorders (e.g., AR vs ADNR, SCAR vs ADNR, etc.). Insome instances, the methods are used to determine whether a transplantrecipient has AR, ADNR or TX. In some instances, the methods are used todetermine whether a transplant recipient has AR, ADNR, SCAR and/or TX,or any subset or combination thereof. In some instances, the methods areused to determine whether a transplant recipient has AR, ADNR, SCAR, TX,HCV, or any subset or combination thereof. As previously described,elevated serum creatinine levels from baseline levels in kidneytransplant recipients may be indicative of AR or ADNR. In preferredembodiments, the methods provided herein are used to distinguish AR fromADNR in a kidney transplant recipient. In some preferred embodiments,the methods provided herein are used to distinguish AR from ADNR in aliver transplant recipient. In some instances, the methods are used todetermine whether a transplant recipient has AR, ADNR, SCAR, TX, acutetransplant dysfunction, transplant dysfunction, transplant dysfunctionwith no rejection, or any subset or combination thereof. In someinstances, the methods provided herein are used to distinguish AR fromHCV from HCV+AR in a liver transplant recipient. In some instances, themethods provided herein are used to distinguish AR from HCV-R fromHCV-R+AR in a liver transplant recipient. In some instances, the methodsprovided herein are used to distinguish HCV-R from HCV-R+AR in a livertransplant recipient. In some instances, the methods provided herein areused to distinguish AR from ADNR from CAN a kidney transplant recipient.

In some instances, the methods are used to distinguish between AR andADNR in a kidney transplant recipient. In some instances, the methodsare used to distinguish between AR and SCAR in a kidney transplantrecipient. In some instances, the methods are used to distinguishbetween AR, TX, and SCAR in a kidney transplant recipient. In someinstances, the methods are used to determine whether a kidney transplantrecipient has AR, ADNR or TX. In some instances, the methods are used todetermine whether a kidney transplant recipient has AR, ADNR, SCAR, CANor TX, or any combination thereof. In some instances, the methods areused to distinguish between AR, ADNR, and CAN in a kidney transplantrecipient.

In some instances, the methods provided herein are used to detect ordiagnose AR in a transplant recipient (e.g., kidney transplantrecipient) in the early stages of AR, in the middle stages of AR, or theend stages of AR. In some instances, the methods provided herein areused to detect or diagnose ADNR in a transplant recipient (e.g., kidneytransplant recipient) in the early stages of ADNR, in the middle stagesof ADNR, or the end stages of ADNR. In some instances, the methods areused to diagnose or detect AR, ADNR, IFTA, CAN, TX, SCAR, or otherdisorders in a transplant recipient with an accuracy, error rate,sensitivity, positive predictive value, or negative predictive valueprovided herein.

Predicting a Condition of a Transplant Recipient

In some embodiments, the methods provided herein can predict AR, CAN,ADNR, and/or SCAR prior to actual onset of the conditions. In someinstances, the methods provided herein can predict AR, IFTA, CAN, ADNR,SCAR or other disorders in a transplant recipient at least 1 day, 5days, 10 days, 30 days, 50 days or 100 days prior to onset. In otherinstances, the methods provided herein can predict AR, IFTA, CAN, ADNR,SCAR or other disorders in a transplant recipient at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, 30 or 31 days prior to onset. In otherinstances, the methods provided herein can predict AR, IFTA, CAN, ADNR,SCAR or other disorders in a transplant recipient at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12 months prior to onset.

Monitoring a Condition of a Transplant Recipient

Provided herein are methods, systems, kits and compositions formonitoring a condition of a transplant recipient. Often, the monitoringis conducted by serial testing, such as serial non-invasive tests,serial minimally-invasive tests (e.g., blood draws), serial invasivetests (biopsies), or some combination thereof. Preferably, themonitoring is conducted by administering serial non-invasive tests orserial minimally-invasive tests (e.g., blood draws).

In some instances, the transplant recipient is monitored as needed usingthe methods described herein. Alternatively the transplant recipient maybe monitored hourly, daily, weekly, monthly, yearly or at anypre-specified intervals. In some instances, the transplant recipient ismonitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 hours. In some instancesthe transplant recipient is monitored at least once every 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30 or 31 days. In some instances, the transplantrecipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12 months. In some instances, the transplant recipient ismonitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years orlonger, for the lifetime of the patient and the graft.

In some instances, gene expression levels in the patients can bemeasured, for example, within, one month, three months, six months, oneyear, two years, five years or ten years after a transplant. In somemethods, gene expression levels are determined at regular intervals,e.g., every 3 months, 6 months or every year post-transplant, eitherindefinitely, or until evidence of a condition is observed, in whichcase the frequency of monitoring is sometimes increased. In somemethods, baseline values of expression levels are determined in asubject before a transplant in combination with determining expressionlevels at one or more time points thereafter.

The results of diagnosing, predicting, or monitoring a condition of atransplant recipient may be useful for informing a therapeutic decisionsuch as determining or monitoring a therapeutic regimen. In someinstances, determining a therapeutic regimen may comprise administeringa therapeutic drug. In some instances, determining a therapeutic regimencomprises modifying, continuing, initiating or stopping a therapeuticregimen. In some instances, determining a therapeutic regimen comprisestreating the disease or condition. In some instances, the therapy is animmunosuppressive therapy. In some instances, the therapy is anantimicrobial therapy. In other instances, diagnosing, predicting, ormonitoring a disease or condition comprises determining the efficacy ofa therapeutic regimen or determining drug resistance to the therapeuticregimen.

Modifying the therapeutic regimen may comprise terminating a therapy.Modifying the therapeutic regimen may comprise altering a dosage of atherapy. Modifying the therapeutic regimen may comprise altering afrequency of a therapy. Modifying the therapeutic regimen may compriseadministering a different therapy. In some instances, the results ofdiagnosing, predicting, or monitoring a condition of a transplantrecipient may be useful for informing a therapeutic decision such asremoval of the transplant. In some instances, the removal of thetransplant can be an immediate removal. In other instances, thetherapeutic decision can be a retransplant. Other examples oftherapeutic regimen can include a blood transfusion in instances wherethe transplant recipient is refractory to immunosuppressive or antibodytherapy.

Examples of therapeutic regimen can include administering compounds oragents that are e.g., compounds or agents having immunosuppressiveproperties (e.g., a calcineurin inhibitor, cyclosporine A or FK 506); amTOR inhibitor (e.g., rapamycin, 40-O-(2-hydroxyethyl)-rapamycin,CCI779, ABT578, AP23573, biolimus-7 or biolimus-9); an ascomycin havingimmuno-suppressive properties (e.g., ABT-281, ASM981, etc.);corticosteroids; cyclophosphamide; azathioprene; methotrexate;leflunomide; mizoribine; mycophenolic acid or salt; mycophenolatemofetil; 15-deoxyspergualine or an immunosuppressive homologue, analogueor derivative thereof; a PKC inhibitor (e.g., as disclosed in WO02/38561 or WO 03/82859); a JAK3 kinase inhibitor (e.g.,N-benzyl-3,4-dihydroxy-benzylidene-cyanoacetamidea-cyano-(3,4-dihydroxy)-]N-benzylcinnamamide (Tyrphostin AG 490),prodigiosin 25-C(PNU156804),[4-(4′-hydroxyphenyl)-amino-6,7-dimethoxyquinazoline] (WHI-P131),[4-(3′-bromo-4′-hydroxylphenyl)-amino-6,7-dimethoxyquinazoline](WHI-P154),[4-(3′,5′-dibromo-4′-hydroxylphenyl)-amino-6,7-dimethoxyquinazoline]WHI-P97, KRX-211,3-{(3R,4R)-4-methyl-3-[methyl-(7H-pyrrolo[2,3-d]pyrimidin-4-yl)-amino]-piperidin-1-yl}-3-oxo-propionitrile,in free form or in a pharmaceutically acceptable salt form, e.g.,mono-citrate (also called CP-690,550), or a compound as disclosed in WO04/052359 or WO 05/066156); a SIP receptor agonist or modulator (e.g.,FTY720 optionally phosphorylated or an analog thereof, e.g.,2-amino-2-[4-(3-benzyloxyphenylthio)-2-chlorophenyl]ethyl-1,3-propanedioloptionally phosphorylated or1-{4-[1-(4-cyclohexyl-3-trifluoromethyl-benzyloxyimino)-ethyl]-2-ethyl-benzyl}-azetidine-3-carboxylicacid or its pharmaceutically acceptable salts); immunosuppressivemonoclonal antibodies (e.g., monoclonal antibodies to leukocytereceptors, e.g., MHC, CD2, CD3, CD4, CD7, CD8, CD25, CD28, CD40, CD45,CD52, CD58, CD80, CD86 or their ligands); other immunomodulatorycompounds (e.g., a recombinant binding molecule having at least aportion of the extracellular domain of CTLA4 or a mutant thereof, e.g.,an at least extracellular portion of CTLA4 or a mutant thereof joined toa non-CTLA4 protein sequence, e.g., CTLA4Ig (for ex. designated ATCC68629) or a mutant thereof, e.g., LEA29Y); adhesion molecule inhibitors(e.g., LFA-1 antagonists, ICAM-1 or -3 antagonists, VCAM-4 antagonistsor VLA-4 antagonists). These compounds or agents may also be used aloneor in combination. Immunosuppressive protocols can differ in differentclinical settings. In some instances, in AR, the first-line treatment ispulse methylprednisolone, 500 to 1000 mg, given intravenously daily for3 to 5 days. In some instances, if this treatment fails, than OKT3 orpolyclonal anti-T cell antibodies will be considered. In otherinstances, if the transplant recipient is still experiencing AR,antithymocyte globulin (ATG) may be used.

Kidney Transplants

The methods, compositions, systems and kits provided herein areparticularly useful for detecting or diagnosing a condition of a kidneytransplant. Kidney transplantation may be needed when a subject issuffering from kidney failure, wherein the kidney failure may be causedby hypertension, diabetes melitus, kidney stone, inherited kidneydisease, inflammatory disease of the nephrons and glomeruli, sideeffects of drug therapy for other diseases, etc. Kidney transplantationmay also be needed by a subject suffering from dysfunction or rejectionof a transplanted kidney.

Kidney function may be assessed by one or more clinical and/orlaboratory tests such as complete blood count (CBC), serum electrolytestests (including sodium, potassium, chloride, bicarbonate, calcium, andphosphorus), blood urea test, blood nitrogen test, serum creatininetest, urine electrolytes tests, urine creatinine test, urine proteintest, urine fractional excretion of sodium (FENA) test, glomerularfiltration rate (GFR) test. Kidney function may also be assessed by arenal biopsy. Kidney function may also be assessed by one or more geneexpression tests. The methods, compositions, systems and kits providedherein may be used in combination with one or more of the kidney testsmentioned herein. The methods, compositions, systems and kits providedherein may be used before or after a kidney transplant. In someinstances, the method may be used in combination with complete bloodcount. In some instances, the method may be used in combination withserum electrolytes (including sodium, potassium, chloride, bicarbonate,calcium, and phosphorus). In some instances, the method may be used incombination with blood urea test. In some instances, the method may beused in combination with blood nitrogen test. In some instances, themethod may be used in combination with a serum creatinine test. In someinstances, the method may be used in combination with urine electrolytestests. In some instances, the method may be used in combination withurine creatinine test. In some instances, the method may be used incombination with urine protein test. In some instances, the method maybe used in combination with urine fractional excretion of sodium (FENA)test. In some instances, the method may be used in combination withglomerular filtration rate (GFR) test. In some instances, the method maybe used in combination with a renal biopsy. In some instances, themethod may be used in combination with one or more other gene expressiontests. In some instances, the method may be used when the result of theserum creatinine test indicates kidney dysfunction and/or transplantrejection. In some instances, the method may be used when the result ofthe glomerular filtration rate (GFR) test indicates kidney dysfunctionand/or transplant rejection. In some instances, the method may be usedwhen the result of the renal biopsy indicates kidney dysfunction and/ortransplant rejection. In some instances, the method may be used when theresult of one or more other gene expression tests indicates kidneydysfunction and/or transplant rejection.

Sensitivity, Specificity, and Accuracy

The methods, kits, and systems disclosed herein for use in identifying,classifying or characterizing a sample may be characterized by having aspecificity of at least about 50%. The specificity of the method may beat least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%,77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The specificity of themethod may be at least about 63%. The specificity of the method may beat least about 68%. The specificity of the method may be at least about72%. The specificity of the method may be at least about 77%. Thespecificity of the method may be at least about 80%. The specificity ofthe method may be at least about 83%. The specificity of the method maybe at least about 87%. The specificity of the method may be at leastabout 90%. The specificity of the method may be at least about 92%.

In some embodiments, the present invention provides a method ofidentifying, classifying or characterizing a sample that gives asensitivity of at least about 50% using the methods disclosed herein.The sensitivity of the method may be at least about 50%, 53%, 55%, 57%,60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%,84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,98%, or 99%. The sensitivity of the method may be at least about 63%.The sensitivity of the method may be at least about 68%. The sensitivityof the method may be at least about 72%. The sensitivity of the methodmay be at least about 77%. The sensitivity of the method may be at leastabout 80%. The sensitivity of the method may be at least about 83%. Thesensitivity of the method may be at least about 87%. The sensitivity ofthe method may be at least about 90%. The sensitivity of the method maybe at least about 92%.

The methods, kits and systems disclosed herein may improve upon theaccuracy of current methods of monitoring or predicting a status oroutcome of an organ transplant. The methods, kits, and systems disclosedherein for use in identifying, classifying or characterizing a samplemay be characterized by having an accuracy of at least about 50%. Theaccuracy of the methods, kits, and systems disclosed herein may be atleast about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%,78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The accuracy of the methods,kits, and systems disclosed herein may be at least about 63%. Theaccuracy of the methods, kits, and systems disclosed herein may be atleast about 68%. The accuracy of the methods, kits, and systemsdisclosed herein may be at least about 72%. The accuracy of the methodmay be at least about 77%. The accuracy of the methods, kits, andsystems disclosed herein may be at least about 80%. The accuracy of themethods, kits, and systems disclosed herein may be at least about 83%.The accuracy of the methods, kits, and systems disclosed herein may beat least about 87%. The accuracy of the methods, kits, and systemsdisclosed herein may be at least about 90%. The accuracy of the methodmay be at least about 92%.

The methods, kits, and/or systems disclosed herein for use inidentifying, classifying or characterizing a sample may be characterizedby having a specificity of at least about 50% and/or a sensitivity of atleast about 50%. The specificity may be at least about 50% and/or thesensitivity may be at least about 70%. The specificity may be at leastabout 70% and/or the sensitivity may be at least about 70%. Thespecificity may be at least about 70% and/or the sensitivity may be atleast about 50%. The specificity may be at least about 60% and/or thesensitivity may be at least about 70%. The specificity may be at leastabout 70% and/or the sensitivity may be at least about 60%. Thespecificity may be at least about 75% and/or the sensitivity may be atleast about 75%.

The methods, kits, and systems for use in identifying, classifying orcharacterizing a sample may be characterized by having a negativepredictive value (NPV) greater than or equal to 90%. The NPV may be atleast about 90%, 91%, 92%, 93%, 94%, 95%, 95.2%, 95.5%, 95.7%, 96%,96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%,99%, 99.2%, 99.5%, 99.7%, or 100%. The NPV may be greater than or equalto 95%. The NPV may be greater than or equal to 96%. The NPV may begreater than or equal to 97%. The NPV may be greater than or equal to98%.

The methods, kits, and/or systems disclosed herein for use inidentifying, classifying or characterizing a sample may be characterizedby having a positive predictive value (PPV) of at least about 30%. ThePPV may be at least about 32%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%,97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%,99.7%, or 100%. The PPV may be greater than or equal to 95%. The PPV maybe greater than or equal to 96%. The PPV may be greater than or equal to97%. The PPV may be greater than or equal to 98%.

The methods, kits, and/or systems disclosed herein for use inidentifying, classifying or characterizing a sample may be characterizedby having a NPV may be at least about 90% and/or a PPV may be at leastabout 30%. The NPV may be at least about 90% and/or the PPV may be atleast about 50%. The NPV may be at least about 90% and/or the PPV may beat least about 70%. The NPV may be at least about 95% and/or the PPV maybe at least about 30%. The NPV may be at least about 95% and/or the PPVmay be at least about 50%. The NPV may be at least about 95% and/or thePPV may be at least about 70%.

The methods, kits, and systems disclosed herein for use in identifying,classifying or characterizing a sample may be characterized by having anerror rate of less than about 30%, 25%, 20%, 19%, 18%, 17%, 16%, 15%,14%, 13%, 12%, 11%, 10%, 9.5%, 9%, 8.5%, 8%, 7.5%, 7%, 6.5%, 6%, 5.5%,5%, 4.5%, 4%, 3.5%, 3%, 2.5%, 2%, 1.5%, or 1%. The methods, kits, andsystems disclosed herein may be characterized by having an error rate ofless than about 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1%or 0.005%. The methods, kits, and systems disclosed herein may becharacterized by having an error rate of less than about 10%. The methodmay be characterized by having an error rate of less than about 5%. Themethods, kits, and systems disclosed herein may be characterized byhaving an error rate of less than about 3%. The methods, kits, andsystems disclosed herein may be characterized by having an error rate ofless than about 1%. The methods, kits, and systems disclosed herein maybe characterized by having an error rate of less than about 0.5%.

The methods, kits, and systems disclosed herein for use in diagnosing,prognosing, and/or monitoring a status or outcome of a transplant in asubject in need thereof may be characterized by having an accuracy of atleast about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%,82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, and systemsdisclosed herein may be characterized by having an accuracy of at leastabout 70%. The methods, kits, and systems disclosed herein may becharacterized by having an accuracy of at least about 80%. The methods,kits, and systems disclosed herein may be characterized by having anaccuracy of at least about 85%. The methods, kits, and systems disclosedherein may be characterized by having an accuracy of at least about 90%.The methods, kits, and systems disclosed herein may be characterized byhaving an accuracy of at least about 95%.

The methods, kits, and systems disclosed herein for use in diagnosing,prognosing, and/or monitoring a status or outcome of a transplant in asubject in need thereof may be characterized by having a specificity ofat least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%,80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, andsystems disclosed herein may be characterized by having a specificity ofat least about 70%. The methods, kits, and systems disclosed herein maybe characterized by having a specificity of at least about 80%. Themethods, kits, and systems disclosed herein may be characterized byhaving a specificity of at least about 85%. The methods, kits, andsystems disclosed herein may be characterized by having a specificity ofat least about 90%. The methods, kits, and systems disclosed herein maybe characterized by having a specificity of at least about 95%.

The methods, kits, and systems disclosed herein for use in diagnosing,prognosing, and/or monitoring a status or outcome of a transplant in asubject in need thereof may be characterized by having a sensitivity ofat least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%,80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, andsystems disclosed herein may be characterized by having a sensitivity ofat least about 70%. The methods, kits, and systems disclosed herein maybe characterized by having a sensitivity of at least about 80%. Themethods, kits, and systems disclosed herein may be characterized byhaving a sensitivity of at least about 85%. The methods, kits, andsystems disclosed herein may be characterized by having a sensitivity ofat least about 90%. The methods, kits, and systems disclosed herein maybe characterized by having a sensitivity of at least about 95%.

The methods, kits, and systems disclosed herein for use in diagnosing,prognosing, and/or monitoring a status or outcome of a transplant in asubject in need thereof may be characterized by having an error rate ofless than about 30%, 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%,11%, 10%, 9.5%, 9%, 8.5%, 8%, 7.5%, 7%, 6.5%, 6%, 5.5%, 5%, 4.5%, 4%,3.5%, 3%, 2.5%, 2%, 1.5%, or 1%. The methods, kits, and systemsdisclosed herein may be characterized by having an error rate of lessthan about 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1% or0.005%. The methods, kits, and systems disclosed herein may becharacterized by having an error rate of less than about 10%. The methodmay be characterized by having an error rate of less than about 5%. Themethods, kits, and systems disclosed herein may be characterized byhaving an error rate of less than about 3%. The methods, kits, andsystems disclosed herein may be characterized by having an error rate ofless than about 1%. The methods, kits, and systems disclosed herein maybe characterized by having an error rate of less than about 0.5%.

The classifier, classifier set, classifier probe set, classificationsystem may be characterized by having a accuracy for distinguishing twoor more conditions (AR, ANDR, TX, CAN) of at least about 50%, 55%, 57%,60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%,95%, or 97%. The classifier, classifier set, classifier probe set,classification system may be characterized by having a sensitivity fordistinguishing two or more conditions (AR, ANDR, TX, CAN) of at leastabout 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, or 97%. The classifier, classifier set,classifier probe set, classification system may be characterized byhaving a selectivity for distinguishing two or more conditions (AR,ANDR, TX, CAN) of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%,72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%.

Computer Program

The methods, kits, and systems disclosed herein may include at least onecomputer program, or use of the same. A computer program may include asequence of instructions, executable in the digital processing device'sCPU, written to perform a specified task. Computer readable instructionsmay be implemented as program modules, such as functions, objects,Application Programming Interfaces (APIs), data structures, and thelike, that perform particular tasks or implement particular abstractdata types. In light of the disclosure provided herein, those of skillin the art will recognize that a computer program may be written invarious versions of various languages.

The functionality of the computer readable instructions may be combinedor distributed as desired in various environments. The computer programwill normally provide a sequence of instructions from one location or aplurality of locations. In various embodiments, a computer programincludes, in part or in whole, one or more web applications, one or moremobile applications, one or more standalone applications, one or moreweb browser plug-ins, extensions, add-ins, or add-ons, or combinationsthereof.

Further disclosed herein are systems for classifying one or more samplesand uses thereof. The system may comprise (a) a digital processingdevice comprising an operating system configured to perform executableinstructions and a memory device; (b) a computer program includinginstructions executable by the digital processing device to classify asample from a subject comprising: (i) a first software module configuredto receive a gene expression profile of one or more genes from thesample from the subject; (ii) a second software module configured toanalyze the gene expression profile from the subject; and (iii) a thirdsoftware module configured to classify the sample from the subject basedon a classification system comprising three or more classes. At leastone of the classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. At leasttwo of the classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. All threeof the classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. Analyzingthe gene expression profile from the subject may comprise applying analgorithm. Analyzing the gene expression profile may comprisenormalizing the gene expression profile from the subject. In someinstances, normalizing the gene expression profile does not comprisequantile normalization.

FIG. 4 shows a computer system (also “system” herein) 401 programmed orotherwise configured for implementing the methods of the disclosure,such as producing a selector set and/or for data analysis. The system401 includes a central processing unit (CPU, also “processor” and“computer processor” herein) 405, which can be a single core or multicore processor, or a plurality of processors for parallel processing.The system 401 also includes memory 410 (e.g., random-access memory,read-only memory, flash memory), electronic storage unit 415 (e.g., harddisk), communications interface 420 (e.g., network adapter) forcommunicating with one or more other systems, and peripheral devices425, such as cache, other memory, data storage and/or electronic displayadapters. The memory 410, storage unit 415, interface 420 and peripheraldevices 425 are in communication with the CPU 405 through acommunications bus (solid lines), such as a motherboard. The storageunit 415 can be a data storage unit (or data repository) for storingdata. The system 401 is operatively coupled to a computer network(“network”) 430 with the aid of the communications interface 420. Thenetwork 430 can be the Internet, an internet and/or extranet, or anintranet and/or extranet that is in communication with the Internet. Thenetwork 430 in some instances is a telecommunication and/or datanetwork. The network 430 can include one or more computer servers, whichcan enable distributed computing, such as cloud computing. The network430 in some instances, with the aid of the system 401, can implement apeer-to-peer network, which may enable devices coupled to the system 401to behave as a client or a server.

The system 401 is in communication with a processing system 435. Theprocessing system 435 can be configured to implement the methodsdisclosed herein. In some examples, the processing system 435 is anucleic acid sequencing system, such as, for example, a next generationsequencing system (e.g., Illumina sequencer, Ion Torrent sequencer,Pacific Biosciences sequencer). The processing system 435 can be incommunication with the system 401 through the network 430, or by direct(e.g., wired, wireless) connection. The processing system 435 can beconfigured for analysis, such as nucleic acid sequence analysis.

Methods as described herein can be implemented by way of machine (orcomputer processor) executable code (or software) stored on anelectronic storage location of the system 401, such as, for example, onthe memory 410 or electronic storage unit 415. During use, the code canbe executed by the processor 405. In some examples, the code can beretrieved from the storage unit 415 and stored on the memory 410 forready access by the processor 405. In some situations, the electronicstorage unit 415 can be precluded, and machine-executable instructionsare stored on memory 410.

Digital Processing Device

The methods, kits, and systems disclosed herein may include a digitalprocessing device, or use of the same. In further embodiments, thedigital processing device includes one or more hardware centralprocessing units (CPU) that carry out the device's functions. In stillfurther embodiments, the digital processing device further comprises anoperating system configured to perform executable instructions. In someembodiments, the digital processing device is optionally connected acomputer network. In further embodiments, the digital processing deviceis optionally connected to the Internet such that it accesses the WorldWide Web. In still further embodiments, the digital processing device isoptionally connected to a cloud computing infrastructure. In otherembodiments, the digital processing device is optionally connected to anintranet. In other embodiments, the digital processing device isoptionally connected to a data storage device.

In accordance with the description herein, suitable digital processingdevices include, by way of non-limiting examples, server computers,desktop computers, laptop computers, notebook computers, sub-notebookcomputers, netbook computers, netpad computers, set-top computers,handheld computers, Internet appliances, mobile smartphones, tabletcomputers, personal digital assistants, video game consoles, andvehicles. Those of skill in the art will recognize that many smartphonesare suitable for use in the system described herein. Those of skill inthe art will also recognize that select televisions, video players, anddigital music players with optional computer network connectivity aresuitable for use in the system described herein. Suitable tabletcomputers include those with booklet, slate, and convertibleconfigurations, known to those of skill in the art.

The digital processing device will normally include an operating systemconfigured to perform executable instructions. The operating system is,for example, software, including programs and data, which manages thedevice's hardware and provides services for execution of applications.Those of skill in the art will recognize that suitable server operatingsystems include, by way of non-limiting examples, FreeBSD, OpenBSD,NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, WindowsServer®, and Novell® NetWare®. Those of skill in the art will recognizethat suitable personal computer operating systems include, by way ofnon-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, andUNIX-like operating systems such as GNU/Linux®. In some embodiments, theoperating system is provided by cloud computing. Those of skill in theart will also recognize that suitable mobile smart phone operatingsystems include, by way of non-limiting examples, Nokia® Symbian® OS,Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®,Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, andPalm® WebOS®.

The device generally includes a storage and/or memory device. Thestorage and/or memory device is one or more physical apparatuses used tostore data or programs on a temporary or permanent basis. In someembodiments, the device is volatile memory and requires power tomaintain stored information. In some embodiments, the device isnon-volatile memory and retains stored information when the digitalprocessing device is not powered. In further embodiments, thenon-volatile memory comprises flash memory. In some embodiments, thenon-volatile memory comprises dynamic random-access memory (DRAM). Insome embodiments, the non-volatile memory comprises ferroelectric randomaccess memory (FRAM). In some embodiments, the non-volatile memorycomprises phase-change random access memory (PRAM). In otherembodiments, the device is a storage device including, by way ofnon-limiting examples, CD-ROMs, DVDs, flash memory devices, magneticdisk drives, magnetic tapes drives, optical disk drives, and cloudcomputing based storage. In further embodiments, the storage and/ormemory device is a combination of devices such as those disclosedherein.

A display to send visual information to a user will normally beinitialized. Examples of displays include a cathode ray tube (CRT, aliquid crystal display (LCD), a thin film transistor liquid crystaldisplay (TFT-LCD, an organic light emitting diode (OLED) display. Invarious further embodiments, on OLED display is a passive-matrix OLED(PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments,the display may be a plasma display, a video projector or a combinationof devices such as those disclosed herein.

The digital processing device would normally include an input device toreceive information from a user. The input device may be, for example, akeyboard, a pointing device including, by way of non-limiting examples,a mouse, trackball, track pad, joystick, game controller, or stylus; atouch screen, or a multi-touch screen, a microphone to capture voice orother sound input, a video camera to capture motion or visual input or acombination of devices such as those disclosed herein.

Non-Transitory Computer Readable Storage Medium

The methods, kits, and systems disclosed herein may include one or morenon-transitory computer readable storage media encoded with a programincluding instructions executable by the operating system to perform andanalyze the test described herein; preferably connected to a networkeddigital processing device. The computer readable storage medium is atangible component of a digital that is optionally removable from thedigital processing device. The computer readable storage mediumincludes, by way of non-limiting examples, CD-ROMs, DVDs, flash memorydevices, solid state memory, magnetic disk drives, magnetic tape drives,optical disk drives, cloud computing systems and services, and the like.In some instances, the program and instructions are permanently,substantially permanently, semi-permanently, or non-transitorily encodedon the media.

A non-transitory computer-readable storage media may be encoded with acomputer program including instructions executable by a processor tocreate or use a classification system. The storage media may comprise(a) a database, in a computer memory, of one or more clinical featuresof two or more control samples, wherein (i) the two or more controlsamples may be from two or more subjects; and (ii) the two or morecontrol samples may be differentially classified based on aclassification system comprising three or more classes; (b) a firstsoftware module configured to compare the one or more clinical featuresof the two or more control samples; and (c) a second software moduleconfigured to produce a classifier set based on the comparison of theone or more clinical features.

At least two of the classes may be selected from transplant rejection,transplant dysfunction with no rejection and normal transplant function.All three classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. Thestorage media may further comprise one or more additional softwaremodules configured to classify a sample from a subject. Classifying thesample from the subject may comprise a classification system comprisingthree or more classes. At least two of the classes may be selected fromtransplant rejection, transplant dysfunction with no rejection andnormal transplant function. All three classes may be selected fromtransplant rejection, transplant dysfunction with no rejection andnormal transplant function.

Web Application

In some embodiments, a computer program includes a web application. Inlight of the disclosure provided herein, those of skill in the art willrecognize that a web application, in various embodiments, utilizes oneor more software frameworks and one or more database systems. In someembodiments, a web application is created upon a software framework suchas Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a webapplication utilizes one or more database systems including, by way ofnon-limiting examples, relational, non-relational, object oriented,associative, and XML database systems. In further embodiments, suitablerelational database systems include, by way of non-limiting examples,Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the artwill also recognize that a web application, in various embodiments, iswritten in one or more versions of one or more languages. A webapplication may be written in one or more markup languages, presentationdefinition languages, client-side scripting languages, server-sidecoding languages, database query languages, or combinations thereof. Insome embodiments, a web application is written to some extent in amarkup language such as Hypertext Markup Language (HTML), ExtensibleHypertext Markup Language (XHTML), or eXtensible Markup Language (XML).In some embodiments, a web application is written to some extent in apresentation definition language such as Cascading Style Sheets (CSS).In some embodiments, a web application is written to some extent in aclient-side scripting language such as Asynchronous Javascript and XML(AJAX), Flash® Actionscript, Javascript, or Silverlight®. In someembodiments, a web application is written to some extent in aserver-side coding language such as Active Server Pages (ASP),ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor(PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In someembodiments, a web application is written to some extent in a databasequery language such as Structured Query Language (SQL). In someembodiments, a web application integrates enterprise server productssuch as IBM® Lotus Domino®. In some embodiments, a web applicationincludes a media player element. In various further embodiments, a mediaplayer element utilizes one or more of many suitable multimediatechnologies including, by way of non-limiting examples, Adobe® Flash®,HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

Mobile Application

In some embodiments, a computer program includes a mobile applicationprovided to a mobile digital processing device. In some embodiments, themobile application is provided to a mobile digital processing device atthe time it is manufactured. In other embodiments, the mobileapplication is provided to a mobile digital processing device via thecomputer network described herein.

In view of the disclosure provided herein, a mobile application iscreated by techniques known to those of skill in the art using hardware,languages, and development environments known to the art. Those of skillin the art will recognize that mobile applications are written inseveral languages. Suitable programming languages include, by way ofnon-limiting examples, C, C++, C#, Objective-C, Java™, Javascript,Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML withor without CSS, or combinations thereof.

Suitable mobile application development environments are available fromseveral sources. Commercially available development environmentsinclude, by way of non-limiting examples, AirplaySDK, alcheMo,Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework,Rhomobile, and WorkLight Mobile Platform. Other development environmentsare available without cost including, by way of non-limiting examples,Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile devicemanufacturers distribute software developer kits including, by way ofnon-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK,BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, andWindows® Mobile SDK.

Those of skill in the art will recognize that several commercial forumsare available for distribution of mobile applications including, by wayof non-limiting examples, Apple® App Store, Android™ Market, BlackBerry®App World, App Store for Palm devices, App Catalog for webOS, Windows®Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, andNintendo® DSi Shop.

Standalone Application

In some embodiments, a computer program includes a standaloneapplication, which is a program that is run as an independent computerprocess, not an add-on to an existing process, e.g., not a plug-in.Those of skill in the art will recognize that standalone applicationsare often compiled. A compiler is a computer program(s) that transformssource code written in a programming language into binary object codesuch as assembly language or machine code. Suitable compiled programminglanguages include, by way of non-limiting examples, C, C++, Objective-C,COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET,or combinations thereof. Compilation is often performed, at least inpart, to create an executable program. In some embodiments, a computerprogram includes one or more executable complied applications.

Web Browser Plug-in

In some embodiments, the computer program includes a web browserplug-in. In computing, a plug-in is one or more software components thatadd specific functionality to a larger software application. Makers ofsoftware applications support plug-ins to enable third-party developersto create abilities which extend an application, to support easilyadding new features, and to reduce the size of an application. Whensupported, plug-ins enable customizing the functionality of a softwareapplication. For example, plug-ins are commonly used in web browsers toplay video, generate interactivity, scan for viruses, and displayparticular file types. Those of skill in the art will be familiar withseveral web browser plug-ins including, Adobe® Flash® Player, Microsoft®Silverlight®, and Apple® QuickTime®. In some embodiments, the toolbarcomprises one or more web browser extensions, add-ins, or add-ons. Insome embodiments, the toolbar comprises one or more explorer bars, toolbands, or desk bands.

In view of the disclosure provided herein, those of skill in the artwill recognize that several plug-in frameworks are available that enabledevelopment of plug-ins in various programming languages, including, byway of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB.NET, or combinations thereof.

Web browsers (also called Internet browsers) are software applications,designed for use with network-connected digital processing devices, forretrieving, presenting, and traversing information resources on theWorld Wide Web. Suitable web browsers include, by way of non-limitingexamples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google®Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. Insome embodiments, the web browser is a mobile web browser. Mobile webbrowsers (also called mircrobrowsers, mini-browsers, and wirelessbrowsers) are designed for use on mobile digital processing devicesincluding, by way of non-limiting examples, handheld computers, tabletcomputers, netbook computers, subnotebook computers, smartphones, musicplayers, personal digital assistants (PDAs), and handheld video gamesystems. Suitable mobile web browsers include, by way of non-limitingexamples, Google® Android® browser, RIM BlackBerry® Browser, Apple®Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® formobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web,Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.

Software Modules

The methods, kits, and systems disclosed herein may include software,server, and/or database modules, or use of the same. In view of thedisclosure provided herein, software modules are created by techniquesknown to those of skill in the art using machines, software, andlanguages known to the art. The software modules disclosed herein areimplemented in a multitude of ways. In various embodiments, a softwaremodule comprises a file, a section of code, a programming object, aprogramming structure, or combinations thereof. In further variousembodiments, a software module comprises a plurality of files, aplurality of sections of code, a plurality of programming objects, aplurality of programming structures, or combinations thereof. In variousembodiments, the one or more software modules comprise, by way ofnon-limiting examples, a web application, a mobile application, and astandalone application. In some embodiments, software modules are in onecomputer program or application. In other embodiments, software modulesare in more than one computer program or application. In someembodiments, software modules are hosted on one machine. In otherembodiments, software modules are hosted on more than one machine. Infurther embodiments, software modules are hosted on cloud computingplatforms. In some embodiments, software modules are hosted on one ormore machines in one location. In other embodiments, software modulesare hosted on one or more machines in more than one location.

Databases

The methods, kits, and systems disclosed herein may comprise one or moredatabases, or use of the same. In view of the disclosure providedherein, those of skill in the art will recognize that many databases aresuitable for storage and retrieval of information pertaining to geneexpression profiles, sequencing data, classifiers, classificationsystems, therapeutic regimens, or a combination thereof. In variousembodiments, suitable databases include, by way of non-limitingexamples, relational databases, non-relational databases, objectoriented databases, object databases, entity-relationship modeldatabases, associative databases, and XML databases. In someembodiments, a database is internet-based. In further embodiments, adatabase is web-based. In still further embodiments, a database is cloudcomputing-based. In other embodiments, a database is based on one ormore local computer storage devices.

Data Transmission

The methods, kits, and systems disclosed herein may be used to transmitone or more reports. The one or more reports may comprise informationpertaining to the classification and/or identification of one or moresamples from one or more subjects. The one or more reports may compriseinformation pertaining to a status or outcome of a transplant in asubject. The one or more reports may comprise information pertaining totherapeutic regimens for use in treating transplant rejection in asubject in need thereof. The one or more reports may compriseinformation pertaining to therapeutic regimens for use in treatingtransplant dysfunction in a subject in need thereof. The one or morereports may comprise information pertaining to therapeutic regimens foruse in suppressing an immune response in a subject in need thereof.

The one or more reports may be transmitted to a subject or a medicalrepresentative of the subject. The medical representative of the subjectmay be a physician, physician's assistant, nurse, or other medicalpersonnel. The medical representative of the subject may be a familymember of the subject. A family member of the subject may be a parent,guardian, child, sibling, aunt, uncle, cousin, or spouse. The medicalrepresentative of the subject may be a legal representative of thesubject.

The term “about,” as used herein and throughout the disclosure,generally refers to a range that may be 15% greater than or 15% lessthan the stated numerical value within the context of the particularusage. For example, “about 10” would include a range from 8.5 to 11.5.

The term “or” as used herein and throughout the disclosure, generallymeans “and/or”.

EXAMPLES

The following illustrative examples are representative of embodiments ofthe software applications, systems, and methods described herein and arenot meant to be limiting in any way.

Example 1

Introduction

Improvements in kidney transplantation have resulted in significantreductions in clinical acute rejection (AR) (8-14%) (Meier-Kriesche etal. 2004, Am J Transplant, 4(3): 378-383). However, histological ARwithout evidence of kidney dysfunction (i.e. subclinical AR) occursin >15% of protocol biopsies done within the first year. Without aprotocol biopsy, patients with subclinical AR would be treated asexcellent functioning transplants (TX). Biopsy studies also documentsignificant rates of progressive interstitial fibrosis and tubularatrophy in >50% of protocol biopsies starting as early as one year posttransplant.

Two factors contribute to AR: the failure to optimize immunosuppressionand individual patient non-adherence. Currently, there is no validatedtest to measure or monitor the adequacy of immunosuppression; thefailure of which is often first manifested directly as an AR episode.Subsequently, inadequate immunosuppression results in chronic rejectionand allograft failure. The current standards for monitoring kidneytransplant function are serum creatinine and estimated glomerularfiltration rates (eGFR). Unfortunately, serum creatinine and eGFR arerelatively insensitive markers requiring significant global injurybefore changing and are influenced by multiple non-immunologicalfactors.

Performing routine protocol biopsies is one strategy to diagnose andtreat AR prior to extensive injury. A study of 28 patients one weekpost-transplant with stable creatinines showed that 21% had unsuspected“borderline” AR and 25% had inflammatory tubulitis (Shapiro et al. 2001,Am J Transplant, 1(1): 47-50). Other studies reveal a 29% prevalence ofsubclinical rejection (Hymes et al. 2009, Pediatric transplantation,13(7): 823-826) and that subclinical rejection with chronic allograftnephropathy was a risk factor for late graft loss (Moreso et al. 2006,Am J Transplant, 6(4): 747-752). A study of 517 renal transplantsfollowed after protocol biopsies showed that finding subclinicalrejection significantly increased the risk of chronic rejection (Moresoet al. 2012, Transplantation 93(1): 41-46).

We originally reported a peripheral blood gene expression signature byDNA microarrays to diagnose AR (Flechner et al. 2004, Am J Transplant,4(9): 1475-1489). Subsequently, others have reported qPCR signatures ofAR in peripheral blood based on genes selected from the literature orusing microarrays (Gibbs et al. 2005, Transpl Immunol, 14(2): 99-108; Liet al. 2012, Am J Transplant, 12(10): 2710-2718; Sabek et al. 2002,Transplantation, 74(5): 701-707; Sarwal et al. 2003, N Engl J Med,349(2): 125-138; Simon et al. 2003, Am J Transplant, 3(9): 1121-1127;Vasconcellos et al. 1998, Transplantation, 66(5): 562-566). As thebiomarker field has evolved, validation requires independently collectedsample cohorts and avoidance of over-training during classifierdiscovery (Lee et al. 2006, Pharm Res, 23(2): 312-328; Chau et at 2008,Clin Cancer Res, 14(19): 5967-5976). Another limitation is that thecurrently published biomarkers are designed for 2-way classifications,AR vs. TX, when many biopsies reveal additional ADNR.

We prospectively followed over 1000 kidney transplants from 5 differentclinical centers (Transplant Genomics Collaborative Group) to identify148 instances of unequivocal biopsy-proven AR (n=63), ADNR (n=39), andTX (n=45). Global gene expression profiling was done on peripheral bloodusing DNA microarrays and robust 3-way class prediction tools (Dabney etal. 2005, Bioinformatics, 21(22): 4148-4154; Shen et al. 2006,Bioinformatics, 22(21): 2635-2642; Zhu et al. 2009, BMC bioinformatics,10 Suppl 1:S21). Classifiers were comprised of the 200 highest valueprobe sets ranked by the prediction accuracies with each tool werecreated with three different classifier tools to insure that our resultswere not subject to bias introduced by a single statistical method.Importantly, even using three different tools, the 200 highest valueprobe set classifiers identified were essentially the same. These 200classifiers had sensitivity, specificity, positive predictive accuracy(PPV), negative predictive accuracy (NPV) and Area Under the Curve (AUC)for the Validation cohort depending on the three different predictiontools used ranging from 82-100%, 76-95%, 76-95%, 79-100%, 84-100% and0.817-0.968, respectively. Next, the Harrell bootstrapping method (Miaoet al. 2013, SAS Global Forum, San Francisco; 2013) based on samplingwith replacement was used to demonstrate that these results, regardlessof the tool used, were not the consequence of statistical over-fitting.Finally, to model the use of our test in real clinical practice, wedeveloped a novel one-by-one prediction strategy in which we created alarge reference set of 118 samples and then randomly took 10 sampleseach from the AR, ADNR and TX cohorts in the Validation set. These werethen blinded to phenotype and each sample was tested by itself againstthe entire reference set to model practice in a real clinical situationwhere there is only a single new patient sample obtained at any giventime.

Materials and Methods

Patient Populations:

We studied 46 kidney transplant patients with well-functioning graftsand biopsy-proven normal histology (TX; controls), 63 patients withbiopsy-proven acute kidney rejection (AR) and 39 patients with acutekidney dysfunction without histological evidence of rejection (ADNR).Inclusion/exclusion criteria are in Table 2. Subjects were enrolledserially as biopsies were performed by 5 different clinical centers(Scripps Clinic, Cleveland Clinic, St. Vincent Medical Center,University of Colorado and Mayo Clinic Arizona). Human Subjects ResearchProtocols approved at each Center and by the Institutional Review Boardof The Scripps Research Institute covered all studies.

Pathology:

All subjects had kidney biopsies (either protocol or “for cause”) gradedfor evidence of acute rejection by the Banff 2007 criteria (Solez et al.2008, Am J Transplant, 8(4): 753-760). All biopsies were read by localpathologists and then reviewed and graded in a blinded fashion by asingle pathologist at an independent center (LG). The local and singlepathologist readings were then reviewed by DRS to standardize andfinalize the phenotypes prior to cohort construction and any diagnosticclassification analysis. C4d staining was done per the judgment of thelocal clinicians and pathologists on 69 of the 148 samples (47%; Table3). Positive was defined as linear, diffuse staining of peritubularcapillaries. Donor specific antibodies were not measured on thesepatients and thus, we cannot exclude the new concept of C4d negativeantibody-mediated rejection (Sis et at 2009, Am J Transplant, 9(10):2312-2323; Wiebe et al. 2012, Am J Transplant, 12(5): 1157-1167).

Gene Expression Profiling and Statistical Analysis:

RNA was extracted from Paxgene tubes using the Paxgene Blood RNA system(PreAnalytix) and GlobinClear (Ambion). Biotinylated cRNA was preparedwith Ambion MessageAmp Biotin H kit (Ambion) and hybridized toAffymetrix Human Genome U133 Plus 2.0 GeneChips. Normalized Signals weregenerated using frozen RMA (fRMA) in R (McCall et al. 2010,Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics,12:369). The complete strategy used to discover, refine and validate thebiomarker panels is shown in FIG. 1. Class predictions were performedwith multiple tools: Nearest Centroids, Support Vector Machines (SVM)and Diagonal Linear Discriminant Analysis (DLDA). Predictive accuracy iscalculated as true positives+true negatives/true positives+falsepositives+false negatives+true negatives. Other diagnostic metrics givenare sensitivity, specificity, Postive Predictive Value (PPV), NegativePredictive Value (NPV) and Area Under the Curve (AUC). ReceiverOperating Characteristic (ROC) curves were generated using pROC in R(Robin et al. 2011, BMC bioinformatics, 12:77). Clinical studyparameters were tested by multivariate logistic regression with anadjusted (Wald test) p-value and a local false discovery ratecalculation (q-value). Chi Square analysis was done using GraphPad. CELfiles and normalized signal intensities are posted in NIH GeneExpression Omnibus (GEO) (accession number GSE15296).

Results

Patient Population

Subjects were consented and biopsied in a random and prospective fashionat five Centers (n=148; Table 3). Blood was collected at the time ofbiopsy. TX represented protocol biopsies of transplants with excellent,stable graft function and normal histology (n=45). AR patients werebiopsied “for cause” based on elevated serum creatinine (n=63). Weexcluded subjects with recurrent kidney disease, BKV or otherinfections. ADNRs were biopsied “for cause” based on suspicion of AR buthad no AR by histology (n=39). Differences in steroid use (less in TX)reflect more protocol biopsies done at a steroid-free center. Asexpected, creatinines were higher in AR and ADNR than TX. Creatinine wasthe only significant variable by multivariable logistic regression byeither phenotype or cohort. C4d staining, when done, was negative in TXand ADNR. C4d staining was done in 56% of AR subjects by the judgment ofthe pathologists and was positive in 1⅔ 6 (33%) of this selected group.

Three-Way Predictions

We randomly split the data from 148 samples into two cohorts, Discoveryand Validationas shown in FIG. 1. Discovery was 32 AR, 20 ADNR, 23 TXand Validation was 32 AR, 19 ADNR, 22 TX. Normalization used FrozenRobust Multichip Average (fRMA) (McCall et al. 2010, Biostatistics,11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). Probesets with median Log₂ signals less than 5.20 in >70% of samples wereeliminated. A 3-class univariate F-test was done on the Discovery cohort(1000 random permutations, FDR <10%; BRB ArrayTools) yielding 2977differentially expressed probe sets using the Hu133 Plus 2.0 cartridgearrays plates (Table 1b). In another experiment, 4132 differentiallyexpressed probe sets were yield using the HT HG-U133+ PM array plates(Table 1d). The Nearest Centroid algorithm (Dabney et al. 2005,Bioinformatics, 21(22): 4148-4154) was used to create a 3-way classifierfor AR, ADNR and TX in the Discovery cohort revealing 200 high-valueprobe sets (Table 1a: using the Hu133 Plus 2.0 cartridge arrays plates;Table 1c: using the HT HG-U133+ PM array plates) defined by having thelowest class predictive error rates (Table 4; see also SupplementalStatistical Methods).

Thustesting our locked classifier in the validation cohort demonstratedpredictive accuracies of 83%, 82% and 90% for the TX vs. AR, TX vs. ADNRand AR vs. ADNR respectively (Table 4). The AUCs for the TX vs. AR, TXvs. ADNR and the AR vs. ADNR comparisons were 0.837, 0.817 and 0.893,respectively as shown in FIG. 5. The sensitivity, specificity, PPV, NPVfor the three comparisons were in similar ranges and are shown in Table4. To determine a possible minimum classifier set, we ranked the 200probe sets by p values and tested the top 25, 50, 100 and 200 (Table 4).The conclusion is that given the highest value classifiers discoveredusing unbiased whole genome profiling, the total number of classifiersnecessary for testing may be 25. However, below that number theperformance of our 3-way classifier falls off to about 50% AUC at 10 orlower (data not shown).

Alternative Prediction Tools

Robust molecular diagnostic strategies should work using multiple tools.Therefore, we repeated the entire 3-way locked discovery and validationprocess using DLDA and Support Vector Machines (Table 5). All the toolsperform nearly equally well with 100-200 classifiers though smalldifferences were observed.

It is also important to test whether a new classifier is subject tostatistical over-fitting that would inflate the claimed predictiveresults. This testing can be done with the method of Harrell et al.using bootstrapping where the original data set is sampled 1000 timeswith replacement and the AUCs calculated for each (Miao et at 2013, SASGlobal Forum, San Francisco; 2013). The original AUCs minus thecalculated AUCs for each tool create the corrections in the AUCs for“optimism” in the original predictions that adjust for potentialover-fitting (Table 6). Therefore we combined the Discovery andValidation cohorts and performed a 3-class univariate F-test on thewhole data set of 148 samples (1000 random permutations, FDR <10%; BRBArrayTools). This yielded 2666 significantly expressed genes from whichwe selected the top 200 by p-values. Results using NC, SVM and DLDA withthese 200 probe sets are shown in Table 6. Optimism-corrected AUCs from0.823-0.843 were obtained for the 200-probe set classifier discoveredwith the 2 cohort-based strategy. Results for the 200-classifier setobtained from the full study sample set of 148 were 0.851-0.866. Theseresults demonstrate that over-fitting is not a major problem as would beexpected from a robust set of classifiers (FIG. 7). These resultstranslate to sensitivity, specificity, PPV and NPV of 81%, 93%, 92% and84% for AR vs. TX; 90%, 85%, 86% and 90% for ADNR vs. TX and 85%, 96%,95% and 87% for AR vs. ADNR.

Validation in One-by-One Predictions

In clinical practice the diagnostic value of a biomarker is challengedeach time a single patient sample is acquired and analyzed. Thus,prediction strategies based on large cohorts of known clinicalclassifications do not address the performance of biomarkers in theirintended application. Two problems exist with cohort-based analysis.First, signal normalization is typically done on the entire cohort,which is not the case in a clinical setting for one patient. Quantilenormalization is a robust method but has 2 drawbacks; it cannot be usedin clinical settings where samples must be processed individually or insmall batches and data sets normalized separately are not comparable.Frozen RMA (fRMA) overcomes these limitations by normalization ofindividual arrays to large publicly available microarray databasesallowing for estimates of probe-specific effects and variances to bepre-computed and “frozen” (McCall et al. 2010, Biostatistics, 11(2):242-253; McCall et al. 2011, BMC bioinformatics, 12:369). The secondproblem with cohort analysis is that all the clinical phenotypes arealready known and classification is done on the entire cohort. Toaddress these challenges, we removed 30 random samples from theValidation cohort (10 AR, 10 ADNR, 10 TX), blinded their classificationsand left a Reference cohort of 118 samples with known phenotypes.Classification was done by adding one blinded sample at a time to theReference cohort. Using the 200-gene, 3-way classifier derived in NC, wedemonstrated an overall predictive accuracy of 80% and individualaccuracies of 80% AR, 90% ADNR and 70% TX and AUCs of 0.885, 0.754 and0.949 for the AR vs. TX, ADNR vs. TX and the AR vs. ADNR comparisons,respectively as shown in FIG. 6.

Discussion

Ideally, molecular markers will serve as early warnings forimmune-mediated injury, before renal function deteriorates, and alsopermit optimization of immunosuppression. We studied a total of 148subjects with biopsy-proven phenotypes identified in 5 differentclinical centers by following over 1000 transplant patients. Global RNAexpression of peripheral blood was used to profile 63 patients withbiopsy-proven AR, 39 patients with ADNR and 46 patients with excellentfunction and normal histology (TX).

We addressed several important and often overlooked aspects of biomarkerdiscovery. To avoid over training, we used a discovery cohort toestablish the predictive equation and its corresponding classifiers,then locked these down and allowed no further modification. We thentested the diagnostic on our validation cohort. To demonstrate therobustness of our approach, we used multiple, publically availableprediction tools to establish that our results are not simplytool-dependent artifacts. We used the bootstrapping method of Harrell tocalculate optimism-corrected AUCs and demonstrated that our predictiveaccuracies are not inflated by over-fitting. We also modeled the actualclinical application of this diagnostic, with a new strategy optimizedto normalizing individual samples by fRMA. We then used 30 blindedsamples from the validation cohort and tested them one-by-one. Finally,we calculated the statistical power of our analysis and determined thatwe have greater than 90% power at a significance level of p<0.001. Weconcluded that peripheral blood gene expression profiling can be used todiagnose AR and ADNR in patients with acute kidney transplantdysfunction. An interesting finding is that we got the same resultsusing the classic two-cohort strategy (discovery vs. validation) as wedid using the entire sample set and creating our classifiers with thesame tools but using the Harrell bootstrapping method to control forover-fitting. Thus, the current thinking that all biomarker signaturesrequire independent validation cohorts may need to be reconsidered.

In the setting of acute kidney transplant dysfunction, we are the firstto address the common clinical challenge of distinguishing AR from ADNRby using 3-way instead of 2-way classification algorithms.

Additional methods may comprise a prospective, blinded study. Thebiomarkers may be further validated using a prospective, blinded study.Methods may comprise additional samples. The additional samples may beused to classify the different subtypes of T cell-mediated,histologically-defined AR. The methods may further comprise use of oneor more biopsies. The one or more biopsies may be used to developdetailed histological phenotyping. The methods may comprise samplesobtained from subjects of different ethnic backgrounds. The methods maycomprise samples obtained from subjects treated with various therapies(e.g., calcineurin inhibitors, mycophenolic acid derivatives, andsteroids. The methods may comprise samples obtained from one or moreclinical centers. The use of samples obtained from two or more clinicalcenters may be used to identify any differences in the sensitivityand/or specificity of the methods to classify and/or characterize one ormore samples. The use of samples obtained from two or more clinicalcenters may be used to determine the effect of race and/or therapy onthe sensitivity and/or specificity of the methods disclosed herein. Theuse of multiple samples may be used to determine the impact of bacterialand/or viral infections on the sensitivity and/or specificity of themethods disclosed herein.

The samples may comprise pure ABMR (antibody mediated rejection). Thesamples may comprise mixed ABMR/TCMR (T-cell mediated rejection). Inthis example, we had 12 mixed ABMR/TCMR instances but only 1 of the 12was misclassified for AR. About 30% of our AR subjects had biopsies withpositive C4d staining. However, supervised clustering to detect outliersdid not indicate that our signatures were influenced by C4d status. Atthe time this study was done it was not common practice to measuredonor-specific antibodies. However, we note the lack of correlation withC4d status for our data.

The methods disclosed herein may be used to determine a mechanism ofADNR since these patients were biopsied based on clinical judgments ofsuspected AR after efforts to exclude common causes of acute transplantdysfunction. While our results from this example do not address thisquestion, it is evident that renal transplant dysfunction is common toboth AR and ADNR. The levels of kidney dysfunction based on serumcreatinines were not significantly different between AR and ADNRsubjects. Thus, these gene expression differences are not based simplyon renal function or renal injury. Also, the biopsy histology for theADNR patients revealed nonspecific and only focal tubular necrosis,interstitial edema, scattered foci of inflammatory cells that did notrise to even borderline AR and nonspecific arteriolar changes consistentbut not diagnostic of CNI toxicity.

Biopsy-based diagnosis may be subject to the challenge of samplingerrors and differences between the interpretations of individualpathologists (Mengel et at 2007, Am J Transplant, 7(10): 2221-2226). Tomitigate this limitation, we used the Banff schema classification and anindependent central biopsy review of all samples to establish thephenotypes. Another question is how these signatures would reflect knowncauses of acute kidney transplant dysfunction (e.g. urinary tractinfection, CMV and BK nephropathy). Our view is that there are alreadywell-established, clinically validated and highly sensitive testsavailable to diagnose each of these. Thus, for implementation andinterpretation of our molecular diagnostic for AR and ADNR clinicianswould often do this kind of laboratory testing in parallel. Incomplicated instances a biopsy will still be required, though we notethat a biopsy is also not definitive for sorting out AR vs. BKnephropathy.

The methods may be used for molecular diagnostics to predict outcomeslike AR, especially diagnose subclinical AR, prior to enough tissueinjury to result in kidney transplant dysfunction. The methods may beused to measure and ultimately optimize the adequacy of long termimmunosuppression by serial monitoring of blood gene expression. Thedesign of the present study involved blood samples collected at the timeof biopsies. The methods may be used to predict AR or ADNR. The absenceof an AR gene profile in a patient sample may be a first measure ofadequate immunosuppression and may be integrated into a serial bloodmonitoring protocol. Demonstrating the diagnosis of subclinical AR andthe predictive capability of our classifiers may create the firstobjective measures of adequate immunosuppression. One potential value ofour approach using global gene expression signatures developed by DNAmicroarrays rather than highly reduced qPCR signatures is that thesemore complicated predictive and immunosuppression adequacy signaturescan be derived later from prospective studies like CTOT08. In turn, anobjective metric for the real-time efficacy of immunosuppression mayallow the individualization of drug therapy and enable the long termserial monitoring necessary to optimize graft survival and minimize drugtoxicity.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention.

Supplemental Statistical Methods

All model selection was done in Partek Genomics Suite v6.6 using thePartek user guide model selection, 2010: Nearest Centroid

The Nearest Centroid classification method was based on [Tibshirani, R.,Hastie, T., Narasimham, B., and Chu, G (2003): Class Prediction byNearest Shrunken Centroids, with Applications to DNA Microarrays.Statist. Sci. Vol. 18 (1):104-117] and [Tou, J. T., and Gonzalez, R. C.(1974): Pattern Recognition Principals, Addison-Wesley, Reading,Massachusetts]. The centroid classifications were done by assigningequal prior probabilities.

Support Vector Machines

Support Vector Machines (SVMs) attempt to find a set of hyperplanes (onefor each pair of classes) that best classify the data. It does this bymaximizing the distance of the hyperplanes to the closest data points onboth sides. Partek uses the one-against-one method as described in “Acomparison of methods for multi-class support vector machines” (C. W.Hsu and C. J. Lin. IEEE Transactions on Neural Networks, 13(2002),415-425).

To run model selection with SVM cost with shrinking was used. Cost of 1to 1000 with Step 100 was chosen to run several models. The radial basiskernel (gamma) was used. The kernel parameters were 1/number of columns.

Diagonal Linear Discriminant Analysis

The Discriminant Analysis method can do predictions based on the classvariable.

The linear with equal prior probability method was chosen.

Linear Discriminant Analysis is performed in Partek using these steps:

-   -   Calculation of a common (pooled) covariance matrix and        within-group means    -   Calculation of the set of linear discriminant functions from the        common covariance and the within-group means    -   Classification using the linear discriminant functions

The common covariance matrix is a pooled estimate of the within-groupcovariance matrices:

ΣSWi

S=i

Σni−Ci

Thus, for linear discriminant analysis, the linear discriminant functionfor class i is defined as: d(x)=−1 (x −m)t S −1 (x −m)+In P(w).

Optimism-Corrected AUC's

The steps for estimating the optimism-corrected AUCs are based on thework of F. Harrell published in [Regression Modeling Strategies: Withapplications to linear models, logistic regression, and survivalanalysis. Springer, New York (2001)].

The basic approach is described in [Miao Y M, Cenzer I S, Kirby K A,Boscardin J W. Estimating Harrell's Optimism on Predictive Indices UsingBootstrap Samples. SAS Global Forum 2013; San Francisco]:

-   -   1. Select the predictors and fit a model using the full dataset        and a particular variable selection method. From that model,        calculate the apparent discrimination (capp).    -   2. Generate M=100 to 200 datasets of the same sample size (n)        using bootstrap samples with replacement.    -   3. For each one of the new datasets m . . . M, select predictors        and fit the model using the exact same algorithmic approach as        in step 1 and calculate the discrimination (cboot (m)).    -   4. For each one of the new models, calculate its discrimination        back on the original data set (corig(m)). For this step, the        regression coefficients can either be fixed to their values from        step 3 to determine the joint degree of over-fitting from both        selection and estimation or can be re-estimated to determine the        degree of over-fitting from selection only.    -   5. For each one of the bootstrap samples, the optimism in the        fit is o(m)=corig(m)−cboot(m). The average of these values is        the optimism of the original model.    -   6. The optimism-corrected performance of the original model is        then cadj=capp−o. This value is a nearly unbiased estimate of        the expected values of the optimism that would be obtained in        external validation.

We adapted this model in Partek Genomics Suite using 1000 samplings withreplacement of our dataset (n=148). An original AUC was calculated onthe full dataset, and then the average of the M=1000 samplings was alsoestimated. The difference between the original and the estimated AUC'swas designated as the optimism and this was subtracted from the originalAUC to arrive at the “optimism-corrected AUC”. In the text, wespecifically compared the AUC's that we reported by testing our locked200-probe set classifiers on only our Validation cohort (see Table 4) tothe optimism-corrected AUC's (see Table 5). The results demonstratelittle difference consistent with the conclusion that our highpredictive accuracies are not the result of over-fitting.

TABLE 1a The top 200 gene probeset used in the 3-Way AR, ADNR TX ANOVAAnalysis (using the Hu133 Plus 2.0 cartridge arrays plates) Geom GeomGeom mean mean mean of in- of in- of in- tensities tensities tensitiesin in in Parametric class class class Pairwise p-value 1 2 3 ProbeSetSymbol Name EntrezID DefinedGenelist significant 1  <1e−07  6.48 7.087.13 212167_ SMARCB1 SWI/SNF 6598 Chromatin (1, 2), (1, 3) s_at related,matrix Remodeling by associated, actin hSWI/SNF ATP- dependent dependentregulator of Complexes chromatin, subfamily b, member 1 2  <1e−07  9.929.42 10.14 201444_ ATP6AP2 ATPase, H+ 10159 (2, 1), (2, 3) s_attransporting, lysosomal accessory protein 2 3  <1e−07  5.21 5.01 5.68227658_ PLEKHA3 pleckstrin 65977 (1, 3), (2, 3) s_at homology domaincontaining, family A (phosphoinositide binding specific) member 3 41.00E−07 6.73 7.62 7.73 201746_at TP53 tumor protein 7157 ApoptoticSignaling (1, 2), (1, 3) p53 in Response to DNA Damage, ATM SignalingPathway, BTG family proteins and cell cycle regulation, Cell Cycle: G1/SCheck Point, Cell Cycle: G2/M Checkpoint, Chaperones modulate interferonSignaling Pathway, CTCF: First Multivalent Nuclear Factor, DoubleStranded RNA Induced Gene Expression, Estrogen- responsive protein Efpcontrols cell cycle and breast tumors growth, Hypoxia and p53 in theCardiovascular system, Overview of telomerase protein component genehTert Transcriptional Regulation, p53 Signaling Pathway, RB TumorSuppressor/Checkpoint Signaling in response to DNA damage, Regulation ofcell cycle progression by Plk3, Regulation of transcriptional activityby PML, Role of BRCA1, BRCA2 and ATR in Cancer Susceptibility,Telomeres, Telomerase, Cellular Aging, and Immortality, Tumor SuppressorArf Inhibits Ribosomal Biogenesis, Amyotrophic lateral sclerosis (ALS),Apoptosis, Cell cycle, Colorectal cancer, Huntington\'s disease, MAPKsignaling pathway, Wnt sign . . . 5 1.00E−07 6.64 6.07 6.87 218292_PRKAG2 protein kinase, 51422 ChREBP regulation (2, 1), (2, 3) s_atAMP-activated, by carbohydrates and gamma 2 non- cAMP, Reversal ofcatalytic subunit Insulin Resistance by Leptin, Adipocytokine signalingpathway, Insulin signaling pathway 6 1.00E−07 11.08 11.9 12.02 1553551_ND2 MTND2 4536 (1, 2), (1, 3) s_at 7 2.00E−07 8.6 9.41 9.21 210996_s_YWHAE tyrosine 3- 7531 Cell cycle (1, 2), (1, 3) at monooxygenase/tryptophan 5- monooxygenase activation protein, epsilon polypeptide 82.00E−07 5.89 6.92 6.23 243037_at (1, 2), (3, 2) 9 2.00E−07 7.61 7 7.81200890_s_ SSR1 signal sequence 6745 (2, 1), (2, 3) at receptor, alpha 102.00E−07 5.18 6.71 6.12 1570571_ CCDC91 coiled-coil 55297 (1, 2), (1, 3)at domain containing 91 11 3.00E−07 7.01 6.55 7.21 233748_ PRKAG2protein kinase, 51422 ChREBP regulation (2, 1), (2, 3) x_atAMP-activated, by carbohydrates and gamma 2 non- cAMP, Reversal ofcatalytic subunit Insulin Resistance by Leptin, Adipocytokine signalingpathway, Insulin signaling pathway 12 3.00E−07 7.96 7.29 7.86 224455_s_ADPGK ADP-dependent 83440 (2, 1), (2, 3) at glucokinase 13 3.00E−07 8.427.93 8.41 223931_s_ CHFR checkpoint with 55743 Tryptophan (2, 1), (2, 3)at forkhead and metabolism ring finger domains, E3 ubiquitin proteinligase 14 3.00E−07 4.84 5.44 5.11 236766_at (1, 2), (1, 3), (3, 2) 153.00E−07 7.95 8.72 7.73 242068_at (1, 2), (3, 2) 16 3.00E−07 6.96 6.587.31 215707_s_ PRNP prion protein 5621 Prion Pathway, (2, 1), (2, 3) atNeurodegenerative Disorders, Prion disease 17 3.00E−07 6.68 7.73 7.41558220_at (1, 2), (1, 3) 18 3.00E−07 6.53 6.19 6.87 203100_s_ CDYLchromodomain 9425 (2, 1), (2, 3) at protein, Y-like 19 3.00E−07 6.195.66 6.28 202278_s_ SPTLC1 serine 10558 Sphingolipid (2, 1), (2, 3) atpalmitoyl- metabolism transferase, long chain base subunit 1 20 4.00E−077.73 7.83 6.71 232726_at (3, 1), (3, 2) 21 4.00E−07 9.78 9.22 9.81218178_s_ CHMP1B charged 57132 (2, 1), (2, 3) at multivesicular bodyprotein 1B 22 4.00E−07 7.08 6.35 7.25 223585_x_ KBTBD2 kelch repeat and25948 (2, 1), (2, 3) at BTB (POZ) domain containing 2 23 4.00E−07 4.854.53 5.49 224407_s_ MST4 serine/threonine 51765 (1, 3), (2, 3) atprotein kinase MST4 24 4.00E−07 9.49 9.74 8.95 239597_at (3, 1), (3, 2)25 4.00E−07 4.3 4.76 4.42 239987_at (1, 2), (3, 2) 26 5.00E−07 5.49 6.065.84 243667_at (1, 2), (1, 3) 27 6.00E−07 8.08 7.32 7.95 209287_s_CDC42EP3 CDC42 effector 10602 (2, 1), (2, 3) at protein (Rho GTPasebinding) 3 28 6.00E−07 7.81 7.17 8 212008_at UBXN4 UBX domain 23190 (2,1), (2, 3) protein 4 29 6.00E−07 4.88 4.57 5.27 206288_at PGGT1B protein5229 (1, 3), (2, 3) geranylgeranyl- transferase type I, beta subunit 306.00E−07 9.75 9.98 9.26 238883_at (3, 1), (3, 2) 31 7.00E−07 6.19 5.426.79 207794_at CCR2 chemokine (C-C 729230 (2, 1), (2, 3) motif) receptor2 32 7.00E−07 8.17 8.58 7.98 242143_at (1, 2), (3, 2) 33 7.00E−07 4.525.01 5.07 205964_at ZNF426 zinc finger 79088 (1, 2), (1, 3) protein 42634 8.00E−07 6.68 5.68 6.75 1553685_ SP1 Sp1 6667 Agrin in Postsynaptic(2, 1), (2, 3) s_at transcription Differentiation, factor Effects ofcalcineurin in Keratinocyte Differentiation, Human Cytomegalovirus andMap Kinase Pathways, Keratinocyte Differentiation, MAPKinase SignalingPathway, Mechanism of Gene Regulation by Peroxisome Proliferators viaPPARa(alpha), Overview of telomerase protein component gene hTertTranscriptional Regulation, Overview of telomerase RNA component genehTerc Transcriptional Regulation, TGF-beta signaling pathway 35 8.00E−075.08 5.8 6.03 219730_at MED18 mediator 54797 (1, 2), (1, 3) complexsubunit 18 36 9.00E−07 5.74 6.07 5.74 233004_x_ (1, 2), (3, 2) at 379.00E−07 5.5 6.4 6.06 242797_x_ (1, 2), (1, 3) at 38 9.00E−07 8.4 8.038.65 200778_s_ 2-Sep septin 2 4735 (2, 1), (2, 3) at 39 1.00E−06 7.646.65 7.91 211559_s_ CCNG2 cyclin G2 901 (2, 1), (2, 3) at 40 1.00E−066.71 7.3 7.23 221090_s_ OGFOD1 2-oxoglutarate 55239 (1, 2), (1, 3) atand iron- dependent oxygenase domain containing 1 41 1.00E−06 4.36 5.144.87 240232_at (1, 2), (1, 3) 42 1.10E−06 6.55 6.86 7.12 221650_s_ MED18mediator 54797 (1, 2), (1, 3), at complex subunit (2, 3) 18 43 1.10E−068 8.48 8.26 214670_at ZKSCAN1 zinc finger with 7586 (1, 2), (1, 3), KRABand (3, 2) SCAN domains 1 44 1.20E−06 6.55 6.17 7.04 202089_s_ SLC39A6solute carrier 25800 (1, 3), (2, 3) at family 39 (zinc transporter),member 6 45 1.20E−06 7.05 6.31 7.45 211825_s_ FLI1 Friend leukemia 2313(2, 1), (2, 3) at virus integration 1 46 1.20E−06 6.05 6.85 6.71243852_at LUC7L2 LUC7-like 2 (S. 51631 (1, 2), (1, 3) cerevisiae) 471.20E−06 8.27 7.44 8.6 207549_x_ CD46 CD46 molecule, 4179 Complement and(2, 1), (2, 3) at complement coagulation cascades regulatory protein 481.30E−06 4.7 5.52 4.65 242737_at (1, 2), (3, 2) 49 1.30E−06 4.93 4.674.57 239189_at CASKIN1 CASK 57524 (2, 1), (3, 1) interacting protein 150 1.30E−06 7.66 8.08 7.47 232180_at UGP2 UDP-glucose 7360 Galactose (1,2), (3, 2) pyrophosphorylase metabolism, 2 Nucleotide sugars metabolism,Pentose and glucuronate interconversions, Starch and sucrose metabolism51 1.40E−06 7.47 6.64 7.31 210971_s_ ARNTL aryl 406 Circadian Rhythms(2, 1), (2, 3) at hydrocarbon receptor nuclear translocator-like 521.40E−06 8.66 8.99 8.1 232307_at (3, 1), (3, 2) 53 1.40E−06 7.06 6.487.56 222699_s_ PLEKHF2 pleckstrin 79666 (2, 1), (2, 3) at homologydomain containing, family F (with FYVE domain) member 2 54 1.60E−06 6.596.62 6.13 234435_at (3, 1), (3, 2) 55 1.60E−06 3.94 3.51 4.17 207117_atZNF117 zinc finger 51351 (2, 1), (2, 3) protein 117 56 1.60E−06 7.577.25 8.26 1553530_ ITGB1 integrin, beta 1 3688 Adhesion and (1, 3), (2,3) a_at (fibronectin Diapedesis of receptor, beta Lymphocytes,polypeptide, Adhesion Molecules antigen CD29 on Lymphocyte, includesMDF2, Agrin in Postsynaptic MSK12) Differentiation, Aspirin BlocksSignaling Pathway Involved in Platelet Activation, B Cell SurvivalPathway, Cells and Molecules involved in local acute inflammatoryresponse, Eph Kinases and ephrins support platelet aggregation, Erk andPI-3 Kinase Are Necessary for Collagen Binding in Corneal Epithelia,Erk1/Erk2 Mapk Signaling pathway, Integrin Signaling Pathway, mCalpainand friends in Cell motility, Monocyte and its Surface Molecules, PTENdependent cell cycle arrest and apoptosis, Ras-Independent pathway in NKcell- mediated cytotoxicity, Signaling of Hepatocyte Growth FactorReceptor, Trefoil Factors Initiate Mucosal Healing, uCalpain and friendsin Cell spread, Axon guidance, Cell adhesion molecules (CAMs), ECM-receptor interaction, Focal adhesion, Leukocyte transendothelialmigration, Regulation of actin cytoskeleton 57 1.60E−06 5.67 5.02 6.16214786_at MAP3K1 mitogen- 4214 Angiotensin II (2, 1), (2, 3) activatedprotein mediated activation of kinase kinase JNK Pathway via kinase 1,E3 Pyk2 dependent ubiquitin protein signaling, BCR ligase SignalingPathway, CD40L Signaling Pathway, Ceramide Signaling Pathway, EGFSignaling Pathway, FAS signaling pathway (CD95), Fc Epsilon Receptor ISignaling in Mast Cells, fMLP induced chemokine gene expression in HMC-1cells, HIV-I Nef: negative effector of Fas and TNF, HumanCytomegalovirus and Map Kinase Pathways, Inhibition of CellularProliferation by Gleevec, Keratinocyte Differentiation, Links betweenPyk2 and Map Kinases, Map Kinase Inactivation of SMRT Corepressor,MAPKinase Signaling Pathway, Neuropeptides VIP and PACAP inhibit theapoptosis of activated T cells, NF- kB Signaling Pathway, p38 MAPKSignaling Pathway, PDGF Signaling Pathway, Rac 1 cell motility signalingpathway, Role of MAL in Rho- Mediated Activation of SRF, Signaltransduction through IL1R, T Cell Receptor Signaling Pathway, The4-1BB-dependent immune response, TNF/Stress Related Signaling, TNFR1Signaling Pathway, TNFR2 Sig . . . 58 1.70E−06 7.49 7.27 7.73 222729_atFBXW7 F-box and WD 55294 Cyclin E Destruction (2, 1), (2, 3) repeatdomain Pathway, containing 7, E3 Neurodegenerative ubiquitin proteinDisorders, Ubiquitin ligase mediated proteolysis 59 1.80E−06 7.73 7.418.08 208310_s_ (2, 1), (2, 3) at 60 1.80E−06 9.06 9.28 8.54 242471_atSRGAP2B SLIT-ROBO 647135 (3, 1), (3, 2) Rho GTPase activating protein 2B61 1.80E−06 7.84 8.15 7.5 238812_at (3, 1), (3, 2) 62 1.80E−06 6.64 7.287.08 206240_s_ ZNF136 zinc finger 7695 (1, 2), (1, 3) at protein 136 631.80E−06 10.29 9.86 10.35 1555797_ ARPC5 actin related 10092 Regulationof actin (2, 1), (2, 3) a_at protein ⅔ cytoskeleton complex, subunit 5,16 kDa 64 1.90E−06 5.05 5.49 5.24 215068_s_ FBXL18 F-box and 80028 (1,2), (3, 2) at leucine-rich repeat protein 18 65 2.00E−06 6.84 6.08 7.16204426_at TMED2 transmembrane 10959 (2, 1), (2, 3) emp24 domaintrafficking protein 2 66 2.00E−06 5.6 5.19 5.13 234125_at (2, 1), (3, 1)67 2.10E−06 9.87 9.38 10.27 200641_s_ YWHAZ tyrosine 3- 7534 Cell cycle(2, 1), (2, 3) at monooxygenase/ tryptophan 5- monooxygenase activationprotein, zeta polypeptide 68 2.10E−06 7.6 6.83 7.89 214544_s_ SNAP23synaptosomal- 8773 SNARE interactions (2, 1), (2, 3) at associated invesicular transport protein, 23 kDa 69 2.10E−06 9.57 10.14 9.29238558_at (1, 2), (3, 2) 70 2.20E−06 6.79 7.12 6.72 221071_at (1, 2),(3, 2) 71 2.40E−06 6.98 6.38 7.29 232591_s_ TMEM30A transmembrane 55754(2, 1), (2, 3) at protein 30A 72 2.40E−06 7.22 7.49 6.35 1569477_ (3,1), (3, 2) at 73 2.60E−06 7.97 7.26 8.22 211574_s_ CD46 CD46 molecule,4179 Complement and (2, 1), (2, 3) at complement coagulation cascadesregulatory protein 74 2.60E−06 7.63 7.37 8.18 201627_s_ INSIG1 insulininduced 3638 (1, 3), (2, 3) at gene 1 75 2.60E−06 5.27 5.97 5.53215866_at (1, 2), (3, 2) 76 2.80E−06 9.6 9.77 9.42 201986_at MED13mediator 9969 (3, 2) complex subunit 13 77 2.80E−06 9.21 8.85 9.44200753_x_ SRSF2 serine/arginine- 6427 Spliceosomal (2, 1), (2, 3) atrich splicing Assembly factor 2 78 3.00E−06 4.65 4.75 5.36 214959_s_API5 apoptosis 8539 (1, 3), (2, 3) at inhibitor 5 79 3.10E−06 7.39 8.287.81 217704_x_ SUZ12P1 suppressor of 440423 (1, 2), (3, 2) at zeste 12homolog pseudogene 1 80 3.30E−06 7.38 7.93 7 244535_at (1, 2), (3, 2) 813.40E−06 7.17 6.66 7.86 210786_s_ FLI1 Friend leukemia 2313 (1, 3), (2,3) at virus integration 1 82 3.40E−06 7.33 7.87 7.47 235035_at SLC35E1solute carrier 79939 (1, 2), (3, 2) family 35, member E1 83 3.40E−0610.42 10.84 10.08 241681_at (1, 2), (3, 2) 84 3.40E−06 7.13 6.16 7.1212720_at PAPOLA poly(A) 10914 Polyadenylation of (2, 1), (2, 3)polymerase mRNA alpha 85 3.50E−06 5.81 5.47 6.03 205408_at MLLT10myeloid/lympho 8028 (2, 1), (2, 3) id or mixed- lineage leukemia(trithorax homolog, Drosophila); translocated to, 10 86 3.50E−06 5.516.12 5.83 238418_at SLC35B4 solute carrier 84912 (1, 2), (1, 3) family35, member B4 87 3.50E−06 6.03 7.01 6.37 1564424_ (1, 2), (3, 2) at 883.60E−06 8.65 9.02 8.39 243030_at (1, 2), (3, 2) 89 3.60E−06 5.52 5.255.78 215207_x_ (2, 1), (2, 3) at 90 3.90E−06 6.77 7.36 7.21 235058_at(1, 2), (1, 3) 91 4.20E−06 8.15 7.94 8.48 202092_s_ ARL2BP ADP- 23568(1, 3), (2, 3) at ribosylation factor-like 2 binding protein 92 4.40E−068.55 8.24 8.6 202162_s_ CNOT8 CCR4-NOT 9337 (2, 1), (2, 3) attranscription complex, subunit 8 93 4.40E−06 8.21 8.1 8.68 201259_s_SYPL1 synaptophysin- 6856 (1, 3), (2, 3) at like 1 94 4.40E−06 7.68 7.797.2 236168_at (3, 1), (3, 2) 95 4.40E−06 6.72 7.58 6.89 1553252_ BRWD3bromodomain 254065 (1, 2), (3, 2) a_at and WD repeat domain containing 396 4.50E−06 6.71 7.67 7.19 244872_at RBBP4 retinoblastoma 5928 The PRC2Complex (1, 2), (3, 2) binding protein Sets Long-term Gene 4 SilencingThrough Modification of Histone Tails 97 4.50E−06 5.58 6.53 6.36215390_at (1, 2), (1, 3) 98 4.60E−06 4.93 6.29 5.36 1566966_ (1, 2), (3,2) at 99 4.90E−06 5.46 5.07 5.68 225700_at GLCCI1 glucocorticoid 113263(2, 1), (2, 3) induced transcript 1 100 5.00E−06 4.96 5.17 4.79236324_at MBP myelin basic 4155 (1, 2), (3, 2) protein 101 5.10E−06 8.087.26 8.33 222846_at RAB8B RAB8B, 51762 (2, 1), (2, 3) member RASoncogene family 102 5.10E−06 6.24 5.75 6.58 1564053_ YTHDF3 YTH domain253943 (2, 1), (2,3) a_at family, member 3 103 5.20E−06 7 6.36 7.35216100_s_ TOR1AIP1 torsin A 26092 (2, 1), (2, 3) at interacting protein1 104 5.20E−06 6.15 5.97 6.63 1565269_ ATF1 activating 466 TNF/StressRelated (1, 3), (2, 3) s_at transcription Signaling factor 1 1055.30E−06 8.13 7.73 8.57 220477_s_ TMEM230 transmembrane 29058 (2, 3) atprotein 230 106 5.30E−06 7.45 8.09 7.72 1559490_ LRCH3 leucine-rich84859 (1, 2), (3, 2) at repeats and calponin homology (CH) domaincontaining 3 107 5.30E−06 7.44 8.05 7.44 225490_at ARID2 AT rich 196528(1, 2), (3, 2) interactive domain 2 (ARID, RFX- like) 108 5.50E−06 7.498.18 7.83 244766_at (1, 2), (3, 2) 109 5.50E−06 7.71 8.41 8 242673_at(1, 2), (3, 2) 110 5.60E−06 8.97 8.59 9.24 202164_s_ CNOT8 CCR4-NOT 9337(2, 1), (2, 3) at transcription complex, subunit 8 111 5.70E−06 7.758.26 7.52 222357_at ZBTB20 zinc finger and 26137 (1, 2), (3, 2) BTBdomain containing 20 112 5.90E−06 5.07 5.52 4.71 240594_at (1, 2), (3,2) 113 6.00E−06 7.78 7.45 7.96 1554577_ PSMD10 proteasome 5716 (2, 1),(2, 3) a_at (prosome, macropain) 26S subunit, non- ATPase, 10 1146.00E−06 6.55 7.03 6.58 215137_at (1, 2), (3, 2) 115 6.10E−06 9.46 9.669.05 243527_at (3, 1), (3, 2) 116 6.30E−06 7.8 7.27 8.15 214449_s_ RHOQras homolog 23433 Insulin signaling (2, 1), (2,3) at family memberpathway Q 117 6.30E−06 7.3 7.92 7.44 216197_at ATF7IP activating 5572(1, 2), (3, 2) transcription factor 7 interacting protein 118 6.40E−067.38 8.17 7.51 1558569_ LOC100131541 uncharacterized 100131541 (1, 2),(3, 2) at LOC100131541 119 6.50E−06 4.79 5.23 4.55 244030_at STYXserine/threonine/ 6815 (1, 3), (2, 3) tyrosine interacting protein 1206.70E−06 7.2 7.95 7.27 244010_at (1, 2), (3, 2) 121 7.20E−06 6.36 6.786.05 232002_at (1, 2), (3, 2) 122 7.20E−06 6.11 6.95 6.18 243051_atCNIH4 cornichon 29097 (1, 2), (3, 2) homolog 4 (Drosophila) 123 7.20E−065.89 6.55 6.32 212394_at EMC1 ER membrane 23065 (1, 2), (1, 3) proteincomplex subunit 1 124 7.30E−06 5.6 6.37 5.86 1553407_ MACF1 microtubule-23499 (1, 2), (3, 2) at actin crosslinking factor 1 125 7.50E−06 5.085.84 5.74 214123_s_ NOP14- NOP14 317648 (1, 2), (1, 3) at AS1 antisenseRNA 1 126 7.50E−06 4.89 5.65 5.06 1564438_ (1, 2), (3, 2) at 1277.60E−06 8.54 8.88 8.22 229858_at (3, 2) 128 7.60E−06 9.26 8.77 9.49215933_s_ HHEX hematopoietically 3087 Maturity onset (2, 1), (2, 3) atexpressed diabetes of the young homeobox 129 7.60E−06 7.97 8.14 7.59239234_at (3, 1), (3, 2) 130 7.70E−06 9.71 9.93 9.17 238619_at (3, 1),(3, 2) 131 7.70E−06 5.46 6.05 5.61 1559039_ DHX36 DEAH (Asp- 170506 (1,2), (3, 2) at Glu-Ala-His) box polypeptide 36 132 7.70E−06 9.19 8.579.36 222859_s_ DAPP1 dual adaptor of 27071 (2, 1), (2, 3) atphosphotyrosine and 3- phosphoinositides 133 7.80E−06 7.76 7.35 8.07210285_x_ WTAP Wilms tumor 1 9589 (2, 1), (2, 3) at associated protein134 7.90E−06 5.64 5.2 5.67 238816_at PSEN1 presenilin 1 5663 Generationof amyloid (2, 1), (2, 3) b-peptide by PS1, g- Secretase mediated ErbB4Signaling Pathway, HIV-I Nef: negative effector of Fas and TNF,Presenilin action in Notch and Wnt signaling, Proteolysis and SignalingPathway of Notch, Alzheimer\'s disease, Neurodegenerative Disorders,Notch signaling pathway, Wnt signaling pathway 135 7.90E−06 5.6 5.425.26 239112_at (2, 1), (3, 1), (3, 2) 136 8.40E−06 6.99 6.66 7.22211536_x_ MAP3K7 mitogen- 6885 ALK in cardiac (2, 1), (2, 3) atactivated protein myocytes, FAS kinase kinase signaling pathway kinase 7(CD95), MAPKinase Signaling Pathway, NFkB activation by NontypeableHemophilus influenzae, NF-kB Signaling Pathway, p38 MAPK SignalingPathway, Signal transduction through IL1R, TGF beta signaling pathway,Thrombin signaling and protease-activated receptors, TNFR1 SignalingPathway, Toll-Like Receptor Pathway, WNT Signaling Pathway, Adherensjunction, MAPK signaling pathway, Toll-like receptor signaling pathway,Wnt signaling pathway 137 8.40E−06 7.82 8.34 7.98 228070_at PPP2R5Eprotein 5529 (1, 2), (3, 2) phosphatase 2, regulatory subunit B′,epsilon isoform 138 8.60E−06 5.38 5.07 5.54 220285_at FAM108B1 familywith 51104 (2, 1), (2,3) sequence similarity 108, member B1 139 8.60E−068.07 7.56 8.2 210284_s_ TAB2 TGF-beta 2311 MAPK signaling (2, 1), (2, 3)at activated kinase pathway, Toll-like 1/MAP3K7 receptor signalingbinding protein pathway 2 140 8.60E−06 5.22 4.5 5.59 1558014_ FAR1 fattyacyl CoA 84188 (2, 1), (2, 3) s_at reductase 1 141 8.60E−06 6.25 6.536.04 240247_at (1, 2), (3, 2) 142 8.80E−06 6.64 6.6 7.21 235177_atMETTL21A methyltransferase 151194 (1, 3), (2, 3) like 21A 143 8.90E−066.46 7.47 6.7 1569540_ (1, 2), (3, 2) at 144 8.90E−06 6.8 6.34 7.16224642_at FYTTD1 forty-two-three 84248 (2, 1), (2, 3) domain containing1 145 8.90E−06 7.94 7.19 8.19 204427_s_ TMED2 transmembrane 10959 (2,1), (2, 3) at emp24 domain trafficking protein 2 146 8.90E−06 9.75 9.999.23 233867_at (3, 1), (3, 2) 147 9.00E−06 10.08 10.61 10.12 212852_s_TROVE2 TROVE domain 6738 (1, 2), (3, 2) at family, member 2 148 9.20E−067.39 7.76 7.12 215221_at (1, 2), (3, 2) 149 9.30E−06 9.17 9.71 9.05231866_at LNPEP leucyl/cystinyl 4012 (1, 2), (3, 2) aminopeptidase 1509.50E−06 5.34 5.61 5.22 217293_at (1, 2), (3, 2) 151 9.50E−06 7.2 6.597.35 224311_s_ CAB39 calcium binding 51719 mTOR signaling (2, 1), (2, 3)at protein 39 pathway 152 9.60E−06 8.5 9 8.52 231716_at RC3H2 ringfinger and 54542 (1, 2), (3, 2) CCCH-type domains 2 153 9.70E−06 6.997.48 6.92 1565692_ (1, 2), (3, 2) at 154 9.70E−06 8.45 8.64 7.76232174_at (3, 1), (3, 2) 155 9.70E−06 6.72 7.22 6.23 243827_at (3, 2)156 9.90E−06 5.13 6.2 5.51 217536_x_ (1, 2), (3, 2) at 157 1.00E−05 98.88 9.33 206052_s_ SLBP stem-loop 7884 (1, 3), (2, 3) at bindingprotein 158 1.00E−05 7.26 6.61 7.55 209131_s_ SNAP23 synaptosomal- 8773SNARE interactions (2, 1), (2, 3) at associated in vesicular transportprotein, 23 kDa 159 1.00E−05 4.46 5.13 4.73 1568801_ VWA9 von Willebrand81556 (1, 2), (3, 2) at factor A domain containing 9 160 1.00E−05 8.017.85 8.32 211061_s_ MGAT2 mannosyl 4247 Glycan structures- (1, 3), (2,3) at (alpha-1,6-)- biosynthesis 1, N- glycoprotein Glycan biosynthesisbeta-1,2-N- acetylglucos- aminyltransferase 161 1.01E−05 8.55 8.23 8.9223010_s_ OCIAD1 OCIA domain 54940 (2, 3) at containing 1 162 1.01E−056.75 7.5 7.6 207460_at GZMM granzyme M 3004 (1, 2), (1, 3) (lymphocytemet-ase 1) 163 1.02E−05 4.77 4.59 5.46 1553176_ SH2D1B SH2 domain 117157Natural killer cell (1, 3), (2, 3) at containing 1B mediatedcytotoxicity 164 1.02E−05 6.36 6.24 6.62 211033_s_ PEX7 peroxisomal 5191(1, 3), (2, 3) at biogenesis factor 7 165 1.04E−05 7.01 7.75 7.77203547_at CD4 CD4 molecule 920 Activation of Csk by (1, 2), (1, 3)cAMP-dependent Protein Kinase Inhibits Signaling through the T CellReceptor, Antigen Dependent B Cell Activation, Bystander B CellActivation, Cytokines and Inflammatory Response, HIV Induced T CellApoptosis, HIV-1 defeats host-mediated resistance by CEM15, IL 17Signaling Pathway, IL 5 Signaling Pathway, Lck and Fyn tyrosine kinasesin initiation of TCR Activation, NO2-dependent IL 12 Pathway in NKcells, Regulation of hematopoiesis by cytokines, Selective expression ofchemokine receptors during T-cell polarization, T Helper Cell SurfaceMolecules, Antigen processing and presentation, Cell adhesion molecules(CAMs), Hematopoietic cell lineage, T cell receptor signaling pathway166 1.04E−05 8.82 8.4 9.05 200776_s_ BZW1 basic leucine 9689 (2, 1), (2,3) at zipper and W2 domains 1 167 1.07E−05 6.71 7.95 7.51 207735_atRNF125 ring finger 54941 (1, 2), (1, 3) protein 125, E3 ubiquitinprotein ligase 168 1.08E−05 6.5 7.08 6.99 46947_at GNL3L guanine 54552(1, 2), (1, 3) nucleotide binding protein- like 3 (nucleolar)-like 1691.08E−05 7.92 8.54 8.12 240166_x_ TRMT10B tRNA 158234 (1, 2), (3, 2) atmethyltransferase 10 homolog B (S. cerevisiae) 170 1.13E−05 8.39 7.968.54 1555780_ RHEB Ras homolog 6009 mTOR Signaling (2, 1), (2, 3) a_atenriched in brain Pathway, Insulin signaling pathway, mTOR signalingpathway 171 1.14E−05 8.37 8.82 8.56 214948_s_ TMF1 TATA element 7110 (1,2), (3,2) at modulatory factor 1 172 1.15E−05 6.77 7.43 7.07 221191_atSTAG3L1 stromal antigen 54441 (1, 2), (3, 2) 3-like 1 173 1.16E−05 5.474.68 5.89 201295_s_ WSB1 WD repeat and 26118 (2, 1), (2, 3) at SOCS boxcontaining 1 174 1.18E−05 7.31 6.73 7.62 211302_s_ PDE4Bphosphodiesterase 5142 Purine metabolism (2, 1), (2, 3) at 4B, cAMP-specific 175 1.19E−05 9.02 9.36 8.68 227576_at (3, 2) 176 1.23E−05 7.347.82 7.28 1553349_ ARID2 AT rich 196528 (1, 2), (3, 2) at interactivedomain 2 (ARID, RFX- like) 177 1.23E−05 8.56 9.02 8.26 242405_at (1, 2),(3, 2) 178 1.24E−05 5.32 6.33 5.71 238723_at ATXN3 ataxin 3 4287 (1, 2),(3, 2) 179 1.25E−05 6.97 7.45 6.59 241508_at (1, 2), (3, 2) 180 1.27E−057.73 7.49 7.74 225374_at (2, 1), (2, 3) 181 1.29E−05 8.64 9.09 8.43244414_at (1, 2), (3, 2) 182 1.29E−05 7.52 6.99 7.57 202213_s_ CUL4Bcullin 4B 8450 (2, 1), (2, 3) at 183 1.29E−05 5.63 5.52 5.19 243002_at(3, 1), (3, 2) 184 1.34E−05 4.51 5.32 4.8 210384_at PRMT2 proteinarginine 3275 Aminophosphonate (1, 2), (3, 2) methyltransferasemetabolism, 2 Androgen and estrogen metabolism, Histidine metabolism,Nitrobenzene degradation, Selenoamino acid metabolism, Tryptophanmetabolism, Tyrosine metabolism 185 1.35E−05 4.65 4.25 4.81 1569952_ (2,1), (2, 3) x_at 186 1.36E−05 12.44 12.13 12.55 202902_s_ CTSS cathepsinS 1520 Antigen processing (2, 1), (2, 3) at and presentation 1871.37E−05 7.56 7.92 7.09 239561_at (3, 2) 188 1.37E−05 6.8 7.38 7.18218555_at ANAPC2 anaphase 29882 Cell cycle, Ubiquitin (1, 2), (1, 3)promoting mediated proteolysis complex subunit 2 189 1.38E−05 8.03 7.898.57 200946_x_ GLUD1 glutamate 2746 Arginine and proline (1, 3), (2, 3)at dehydrogenase 1 metabolism, D- Glutamine and D- glutamate metabolism,Glutamate metabolism, Nitrogen metabolism, Urea cycle and metabolism ofamino groups 190 1.39E−05 5.15 4.77 5.96 221268_s SGPP1 sphingosine-1-81537 Sphingolipid (1, 3), (2, 3) _at phosphate metabolism phosphatase 1191 1.40E−05 5.36 6.5 5.8 216166_at (1, 2), (3, 2) 192 1.41E−05 7.077.64 7.09 1553909_ FAM178A family with 55719 (1, 2), (3, 2) x_atsequence similarity 178, member A 193 1.42E−05 7.23 6.95 7.5 1554747_2-Sep septin 2 4735 (2, 3) a_at 194 1.45E−05 6.04 6.69 6.38 242751_at(1, 2) 195 1.46E−05 7.74 8.22 7.79 239363_at (1, 2), (3, 2) 196 1.47E−055.57 5.19 5.76 222645_s_ KCTD5 potassium 54442 (2, 1), (2, 3) at channeltetramerisation domain containing 5 197 1.53E−05 3.93 3.73 4.26210875_s_ ZEB1 zinc finger E- 6935 SUMOylation as a (1, 3), (2, 3) atbox binding mechanism to homeobox 1 modulate CtBP- dependent generesponses 198 1.55E−05 8.42 8 8.63 1567458_ RAC1 ras-related C3 5879Agrin in Postsynaptic (2, 1), (2, 3) s_at botulinum toxinDifferentiation, substrate 1 (rho Angiotensin II family, small mediatedactivation of GTP binding JNK Pathway via protein Rac1) Pyk2 dependentsignaling, BCR Signaling Pathway, fMLP induced chemokine gene expressionin HMC-1 cells, How does salmonella hijack a cell, Influence of Ras andRho proteins on G1 to S Transition, Links between Pyk2 and Map Kinases,MAPKinase Signaling Pathway, p38 MAPK Signaling Pathway,Phosphoinositides and their downstream targets., Phospholipids assignalling intermediaries, Rac 1 cell motility signaling pathway, RasSignaling Pathway, Ras-Independent pathway in NK cell- mediatedcytotoxicity, Role of MAL in Rho- Mediated Activation of SRF, Role ofPI3K subunit p85 in regulation of Actin Organization and Cell Migration,T Cell Receptor Signaling Pathway, Transcription factor CREB and itsextracellular signals, Tumor Suppressor Arf Inhibits RibosomalBiogenesis, uCalpain and friends in Cell spread, Y branching of actinfilaments, Adherens junction, Axon guidance, B cell receptor signalingpat . . . 199 1.56E−05 9 9.37 8.89 233893_s_ UVSSA UV-stimulated 57654(1, 2), (3, 2) at scaffold protein A 200 1.59E−05 5.95 6.42 6.55226539_s_ (1, 2), (1, 3) at

TABLE 1b The 2977 gene probeset used in the 3-Way AR, ADNR TX ANOVAAnalysis (using the Hu133 Plus 2.0 cartridge arrays plates) SMARCB1233900_at RNPC3 FLJ44342 1563715_at 244088_at ATP6AP2 KCNC4 COQ9 CNIHZBTB20 OXTR PLEKHA3 GLRX3 HSPD1 HMGXB4 BLOC1S3 220687_at TP53 BCL2L11NFYA GJC2 GMCL1 TLE4 PRKAG2 HNRPDL TNRC18 236370_at SRSF3 1556655_s_atND2 XRN2 238745_at SH2B3 227730_at TMEM161B YWHAE EXOC3 215595_x_at232834_at CLIP1 FTSJD1 243037_at RNGTT 228799_at OTUB2 236742_at AAGABSSR1 ENY2 1555485_s_at LINC00028 MAT2B 1570335_at CCDC91 KLHL36 SEMA7AINTS1 217185_s_at CIZ1 PRKAG2 UBR4 ZNF160 DIS3 MTUS2 ZNF45 ADPGK RALBG6PD ATP2A3 MOB3B 215650_at CHFR UBE2D3 1556205_at LOC1001288221558922_at UBTF 236766_at DR1 DSTN PRKAR2A 233376_at ZNF555 242068_atCLEC7A HSF1 214027_x_at SLC16A7 MED28 PRNP SDHD NPHP4 TCF4 241106_atSIGLEC10 1558220_at 1565701_at 242126_at CCDC115 IRAK3 TM6SF2 CDYL216813_at SRSF11 244677_at 242362_at PACSIN2 SPTLC1 C2orf72 231576_atSRP72 ADAMTS16 DLX3 232726_at 236109_at RBM4 221770_at KIAA1715 PKMCHMP1B 240262_at FAM175B 1556003_a_at 202648_at SUMO3 KBTBD2 1563364_atZNF592 1559691_at PTPRO GATM MST4 235263_at RAPGEF2 KANK2 ADAM12 NECAP1239597_at PRR12 MALAT1 PEX7 XBP1 LPPR3 239987_at CBFB 216621_at PTGS2222180_at CAMK2D 243667_at JAK1 KIAA1683 RDH11 MGAT2 CRH CDC42EP3240527_at 1559391_s_at PHF7 CUBN 243310_at UBXN4 6-Sep DR1 236446_atZNF626 DYNC1H1 PGGT1B ITGB1 ARF4 242027_at CD84 C17orf80 238883_at1562948_at RAB39B 1565877_at LRRC8C 233931_at CCR2 YRDC GPR27 ACBD3 AMBPC20orf78 242143_at 1566001_at HPS3 SNAI3-AS1 C3orf38 MED13L ZNF426 SETD5215900_at 215630_at ATG4C 226587_at SP1 RNGTT 220458_at POLR1B217701_x_at 226543_at MED18 240789_at ARFGAP3 PON2 LOC100996246 LPP233004_x_at 239227_at 1566428_at MED27 244473_at LOC100131043242797_x_at 236802_at EID2B LOC100289058 FOLR1 AGA 2-Sep TM2D11559347_at 1565976_at 231682_at STX11 CCNG2 TBX4 ARPP19 YEATS4 NRP2215586_at OGFOD1 232528_at IFRD1 VIPR2 PTPRD 244177_at 240232_at TSEN151564227_at 233761_at RBBP6 240892_at MED18 MAP3K8 1561058_at SLC25A16SMARCD2 GPBP1L1 ZKSCAN1 TMEM206 SFT2D3 OSTC CLEC4A 242872_at SLC39A6239600_at LOC646482 ATP11B RBM3 240550_at FLI1 TMOD3 TMEM43 C1orf43POLE3 KIAA0754 LUC7L2 GLS CSNK1A1 CASK MAPK9 213704_at CD46 RPS10233816_at PAXBP1 ACTR10 GABPA 242737_at TSN C3orf17 ADSSL1 1557551_at240347_at CASKIN1 C7orf53 239901_at C11orf58 ZNF439 G3BP1 UGP2 MTMR1ABCC6 1569312_at RTFDC1 PIK3AP1 ARNTL TNPO1 7-Mar STRN ND6 KRIT1232307_at 232882_at NRIP2 243634_at LIN7C CGNL1 PLEKHF2 SSR1 1569930_at240392_at CNOT7 C11orf30 234435_at 233223_at DLAT SDK2 237846_at1562059_at ZNF117 VNN3 USP14 WNK1 GAPVD1 SPG20 ITGB1 ARFGEF1 UACA AKAP10CDAN1 1561749_at MAP3K1 244100_at PGK1 UNK TECR C16orf87 FBXW7 TMEM245CHRNB2 GUCA1B LINC00476 232234_at 208310_s_at CDC42EP3 SUMF2 PTPRH229448_at MEGF9 SRGAP2B 244433_at NEDD1 216756_at ZBTB43 RAD18 238812_atMYCBP2 MFN1 1557993_at 230659_at ZCCHC3 ZNF136 ATF5 PIGY MAEA1560199_x_at TOR2A ARPC5 RBBP4 1564248_at 215628_x_at BET1L SMIM14FBXL18 232929_at APH1A SPSB1 PAFAH1B1 SLMO2 TMED2 PCGF1 DCTN1 APC243350_at NAA15 234125_at 239567_at KLHL42 TIMM50 TAF1B ZNF80 YWHAZ233799_at OGFRL1 240080_at MRPL42 GKAP1 SNAP23 FBXO9 FBXL14 SLC38A9232867_at FBXO8 238558_at SS18 240013_at MON1A ASIC4 233027_at 221071_atARMC10 SREK1IP1 239809_at WNT7B 237051_at TMEM30A 232700_at CDH16243088_at ZNF652 YJEFN3 1569477_at UNKL 234278_at 233940_at GMEB1213048_s_at CD46 1561389_at LIX1L KIAA1468 BCAS4 PPP1R12A INSIG1 SLC35B3NBR2 1563320_at GFM1 TCEB3 215866_at 235912_at SEC31A APOL2 IGHMBP2201380_at MED13 234759_at MLX FLJ12334 BEX4 LOC283788 SRSF2 SLC15A4ATP6V1H FANCF SNX24 RNA45S5 API5 TMEM70 USP36 242403_at AKIRIN1 RBBP4SUZ12P1 RANBP9 C2orf68 ERLIN2 ZNF264 SNTB2 244535_at NMD3 LOC149373241965_at LYSMD2 234046_at FLI1 LARP1 TMEM38B SLC30A5 RUFY2 231346_s_atSLC35E1 240765_at TNPO1 238024_at 239925_at AGFG2 241681_at SCFD1 RSU11562283_at SPSB4 NONO PAPOLA RAB28 FXR1 216902_s_at ZNF879 THEMIS MLLT10242144_at TET3 SMAP1 XIAP NDUFS1 SLC35B4 PAFAH2 MGC57346 C6orf89 SNX19RAB18 1564424_at ZNF43 231107_at FBXO33 RER1 NAP1L1 243030_at CYP4V2PHF20 ANKRD10 1566472_s_at 235701_at 215207_x_at MREG 1560332_at INSIG1QSOX2 1557353_at 235058_at BECN1 PAIP2 239106_at DDR2 HOPX ARL2BP LOXL2235805_at KIAA1161 1566491_at CCT6A CNOT8 TPD52 KRBA2 1563590_at PRNPVGLL4 SYPL1 API5 BCKDHB LOC100129175 HNMT EXOSC6 236168_at 222282_atVAV3 DLGAP4 GGA1 TM2D3 BRWD3 HHEX MC1R ARID5A AQR BAZ2A RBBP4 237048_atHMGCR SMAD6 KIF3B HIF1AN 215390_at 1562853_x_at MBD4 ZNF45 SMO MBD41566966_at CCNL1 PPP2R2A SLC38A10 241303_x_at DET1 GLCCI1 TIGD1242480_at GFM1 MTPAP 227383_at MBP 241391_at INTS9 221579_s_at 230998_at239005_at RAB8B DDX3X ABHD6 CCIN 241159_x_at PIGF YTHDF3 FTX CEP350ASAP1-IT1 TGDS SREBF2 TOR1AIP1 HIBADH RHEB FAM126B CRLS1 TLE4 ATF1 PXK1559598_at 232906_at REXO1 PLCB1 TMEM230 231644_at ING4 RALGAPA21557699_x_at DNAAF2 LRCH3 FPR2 RHNO1 SPCS1 FAM208B PALLD ARID2 CPSF21552867_at MAGT1 ZNF721 USP28 244766_at G2E3 212117_at TMEM19 PRDM11217572_at 242673_at 237600_at TLR8 ZNF24 243869_at C1orf86 CNOT8244357_at 231324_at NAIP G2E3 DAAM2 ZBTB20 215577_at DDA1 232134_at233270_x_at GNB3 240594_at CLEC7A 243207_at B2M DMPK RNF145 PSMD10GAREML 9-Sep NPR3 NLK RANBP9 215137_at 1566965_at SRP72 ATP6AP2 TRIM281557543_at 243527_at TLK1 233832_at 1563104_at 238785_at ZNF250 RHOQ231281_at GABBR1 ERCC3 243691_at PPP1R15B ATF7IP EXOC5 TRIM50 STEAP4LOC283482 FLI1 LOC100131541 GHITM KIAA1704 232937_at LOC285300 SMURF1STYX ZCCHC9 LRRFIP2 238892_at 242310_at EAF1 244010_at ZNF330 LIG4NDUFS4 239449_at SUMO2 232002_at TMEM230 HECA ERLEC1 SOCS5 MED21 CNIH4ZNF207 243561_at UBL3 233121_at PIGR EMC1 LOC440993 215961_at BBS12NUMBL 240154_at MACF1 PAPOLA BRAP 242637_at PCNP UPF1 NOP14-AS1 CXorf36SURF4 231125_at PRG3 217703_x_at 1564438_at AKAP13 PANK2 SMCHD1 SRSF2SKAP2 229858_at ASXL2 236338_at PDE3B MOAP1 ARL6IP5 HHEX RAB14 FNBP1TRPM7 SPG11 STIM2 239234_at DIP2A G2E3 RPL18 ZFP41 WBP11 238619_at243391_x_at FLT3LG PTOV1 CARD11 ADAM10 DHX36 PBXIP1 233800_at HAVCR2MFSD8 PHF20L1 DAPP1 MFN1 SMIM7 242890_at EEF1D CR1 WTAP DENND4C ADRA2BBFAR NAPEPLD STEAP4 PSEN1 SLC25A40 PTPN23 ARHGAP21 GM2A MAML2 239112_at242490_at SNX2 SRPK2 EBAG9 236450_at MAP3K7 LMBR1 P2RY10 PRKCA 242749_atRTN4IP1 PPP2R5E CDK6 ABHD15 TRAPPC2L PRKCA 221454_at FAM108B1 TMEM191565804_at LRRFIP1 SUV39H1 RMDN2 TAB2 237310_at 236966_at CCDC6LOC100507281 UFM1 FAR1 PIK3C3 SRPK2 232344_at PLXDC1 228911_at 240247_atABI1 242865_at TRAK1 ZP1 BEST1 METTL21A CHD8 AGMAT SRGN HR 239086_at1569540_at SLC30A5 GPHN PAPOLA RYBP 216380_x_at FYTTD1 216056_at ZNF75DATXN7L1 POPDC2 KCTD5 TMED2 MAPKAPK5 SON TBATA SNX2 SFTPC 233867_atCSNK1A1 AP1AR TTC17 BMP7 KLF7 TROVE2 243149_at TXNDC12 ELP6 226532_atERBB2IP 215221_at 1560349_at 1569527_at CACHD1 MAN2A2 232622_at LNPEPSERBP1 237377_at ELOVL5 LOC100129726 234882_at 217293_at MDM4 NPTN1563173_at PDE1B 225494_at CAB39 217702_at 239431_at CHRNA6 SCRN3 OSGIN1RC3H2 RPAP3 242132_x_at SLC9A5 FAM3C SLC26A6 1565692_at NR3C1 AP1S2UBE4B GPHA2 MALT1 232174_at SMAD4 TMEM38B 233674_at USP38 216593_s_at243827_at FBXO11 CDC42SE1 CHRNE IDH3A 237176_at 217536_x_at SNAPC3 TLR4TIMM23 FGFR1OP2 1570087_at SLBP SVIL C14orf169 FCGR2C DST ENTPD2 SNAP23TSPAN14 BTF3L4 232583_at NSMF 232744_x_at VWA9 SERBP1 AUH KIAA2026241786_at NUDT6 MGAT2 238544_at RDX 242551_at WHSC1L1 AGPAT1 OCIAD1LRRFIP2 224175_s_at MALAT1 234033_at 242926_at GZMM SNAP29 240813_at229434_at 244086_at CLASP2 SH2D1B 215648_at FGL1 YWHAE C14orf142239241_at PEX7 PPTC7 235028_at COPS8 ME2 MIR143HG CD4 241932_at SENP7215599_at NFYB 232472_at BZW1 CNBP 215386_at KDM2A AIF1L FCAR RNF125239463_at MGC34796 TFAP2D MALT1 XPNPEP3 GNL3L POGZ ME2 PKHD1 226250_atACAD8 TRMT10B 215083_at PPP6R1 OGFOD1 233099_at PARP15 RHEB TMEM64211180_x_at GPATCH2L 237655_at TMEM128 TMF1 ERP44 GFM1 DSTN ZBED3 PTPN7STAG3L1 LOC100272216 CEP120 ZSCAN9 BAZ1B 215474_at WSB1 1562062_atMAN1A2 MFSD11 CDC42SE2 215908_at PDE4B 1559154_at STX3 SERPINI1 ISG20AASDHPPT 227576_at CDC40 243469_at XRCC3 KLHDC8A NCBP1 ARID2 PIGM NDUFS1ADNP DLAT 233272_at 242405_at 238000_at CAAP1 LOC100506651 DIABLO240870_at ATXN3 RAP2B CHD4 242532_at PDXDC1 LPIN1 241508_at 236685_atZNF644 CD200R1 PTGDR AFG3L1P 225374_at GLIPR1 FLJ13197 ZMYND11231992_x_at CNEP1R1 244414_at OGT HSPA14 PACRGL 221381_s_at PMS2P5 CUL4BCREB1 CNNM2 RNFT1 ZNF277 C14orf1 243002_at YAF2 SENP2 CD58 RBM47 THOP1PRMT2 215385_at KLF4 USP42 SYN1 CLASP2 1569952_x_at PPM1K 1558410_s_at216745_x_at SCP2 241477_at CTSS 1562324_a_at 241837_at 235493_at234159_at 233733_at 239561_at SH2D1B LOC100130654 CLASP1 FLJ10038 SNX19ANAPC2 GNAI3 SNTB2 HACL1 FAM84B ZNF554 GLUD1 AKAP11 233417_at ANAPC7BRAP G2E3 SGPP1 1569578_at 236149_at LOC286437 FOXJ1 SLC30A1 216166_atATF7 237404_at RPS12 244845_at ATF7IP FAM178A 234260_at DHRS4-AS1 PALLD244550_at 228746_s_at 2-Sep KLHDC10 KHSRP MTO1 244422_at 232779_at242751_at RFWD3 SLC25A43 241114_s_at RAB30 227052_at 239363_at STX16MESP1 UBXN7 TTLL5 240405_at KCTD5 CTBP2 PSMD12 215376_at NUCKS1 ZNF408ZEB1 FOXN3 HMP19 241843_at PRMT8 PIP5K1A RAC1 239861_at 240020_at TSR1239659_at CCPG1 UVSSA LOC100505876 LRRFIP1 CD164 CENPL ASCC1 226539_s_atMLLT10 TNPO1 MRPS10 THADA LINC00527 MS4A6A 243874_at GALNT7 MACF1 MRPS11UBQLN4 CNOT4 MDM4 VPS37A 213833_x_at DBH MAST4 1559491_at TAOK1 PPP2CA244665_at LRRFIP1 RAP1GAP NOP16 PIK3R5 TFEC HPN 239445_at SRSF4 HIRIP3LINC00094 ENTPD6 PAK2 234753_x_at LOC149401 PTPN11 242369_x_at M6PRMOB1A TSHZ2 11-Sep GFM2 242357_x_at TAF9B MTMR9LP GTSE1 NIT1 SMCR8243035_at 1564077_at IGLJ3 243674_at PTGS2 ZNF688 TMEM50B PMAIP1 ADNP2237201_at ACTG1 KIAA0485 XAGE3 234596_at PLA2G4F HSP90AB1 ARHGEF1 ABHD10FAS 1558748_at NRBF2 216285_at TRIM8 LOC729013 FBXO9 TMEM41A SYTL3 ACTR3TTC27 233440_at 239655_at MIER3 C1orf174 KRCC1 GABPA 224173_s_at VAMP3UBAP2L C16orf72 RHOA 243736_at TAOK1 230918_at NXT2 ZNF836 TRMT2B TLR2SLC39A6 ZNF615 PRRT3 KCNK3 ADK VPS35 NAMPT DLST BRD3 UBXN8 PNRC1 MED28CNBP 231042_s_at 243414_at TIA1 216607_s_at 242542_at MBP TNPO3 MTMR2NCOA2 GRM2 241893_at PRKAR2A CS 243178_at CEP135 EIF3L ZNF92 GNL3LCLTC-IT1 TCF3 BCL7B 236704_at GPC2 YWHAZ MEF2C SFTPB HELB MYO9B TSPAN16PPP6R2 PACSIN3 UBE2N LOC642236 NGFRAP1 LOC100507602 RSPRY1 TROVE2238836_at PAK2 PSPC1 1561155_at MBNL1 PPIF RPRD1A FPR2 239560_at218458_at DENR RBM15B RDH11 ITGA4 ERCC8 ZNF548 DNAJB9 242793_at WWP2MSI2 VPS13D ADAM17 216766_at ARHGAP32 CLYBL SMPD1 MTM1 1561067_at CLINT1SHOX2 230240_at TMEM92 ANKRD13D 207186_s_at FBXO9 208811_s_at VNN3ARHGAP26 AKAP17A MED29 ATXN1 217055_x_at PECR ZBTB1 LOC100506748 DTD21570021_at LMNA HLA-DPA1 GALNT9 RCN2 242695_at ARFGEF1 PSEN1 TPCN2CTNNB1 NTN5 238040_at 232333_at CLCN7 LARS 227608_at OAT EIF1B-AS1 GOSR1C15orf37 ZDHHC8 TMEM106C ZNF562 1561181_at FAM73A TUG1 BCL6 PSD41560049_at SERTAD2 244358_at WDYHV1 IMPACT DYNLRB1 215861_at 1561733_atSPPL3 SELT 1558236_at 237868_x_at A2M FGFR1 ARFIP1 BBIP1 235295_at PADI2CADM3 PSMD10 SF3B3 TMCO3 APOBEC2 RALGPS1 CAMK2D CCND3 TMEM185A FBXO22PIK3CG ZNF790-AS1 TRIM4 DPYSL4 LARP1 PICALM ZNF706 214194_at CRYZL1ANGPTL4 SETX KLHL7 239396_at FTCD IL1R2 CAPS POLR3A 233431_x_at GATAD2B1570299_at WNT10A 238159_at RSBN1 TNPO3 ZDHHC21 WDR20 SEH1L 225239_atSNX13 SMAD4 MED4 208638_at 216789_at STOX2 ZNF542 FAS PRKACB 9-Sep JAK3207759_s_at 228623_at RPL27A HERPUD1 KLHL20 TFDP2 LOC100996349 242688_at222295_x_at PPFIA3 SOS2 C4orf29 ZNF805 237881_at GHITM 222358_x_atPTPN22 TACC2 LOC100131825 239414_at IER5 TUBGCP4 CYB5R4 FBXO33 ZFP36L2RASSF5 HSPD1 210338_s_at 232601_at GOPC KIF1B 222319_at UEVLD RABL5IMPAD1 C1QBP ZNF117 PPP1R3B LPXN 242859_at LOC100128751 NXPH3 SLC35D2TAOK1 DENND2A 1554948_at CXCR4 PLAUR 235441_at MAP2K4 SPRTN ZNF551PODNL1 ZNF175 214996_at NMT2 SF3A2 GPSM3 ZNF567 POLR2L LOC100506127STX16 DICER1 1556339_a_at 242839_at ZAN BTBD7 PDE7A ANKRD17 236545_atMAPRE2 239296_at PNRC2 RALGAPA2 GOLGA7 MKRN2 PGM2 TBX2 232991_at MED1UBE2J1 AGAP2 PHF20L1 TUBB PRKCSH PICALM 215212_at CCNK TTF1 244847_atPRM3 BTG2 236944_at 233103_at ETV4 MEA1 GNAI3 KIAA2018 CREB1 239408_atFAM159A MLL 243839_s_at 230590_at 232835_at NXPE3 EIF4E SECISBP2L PHTF2AP5M1 LOC100631377 222306_at 232535_at MCRS1 TSPYL5 217615_at ZNF880ANGPTL1 UBA1 219422_at DCAF17 238519_at TMEM63A RYK ZNF493 USP7 IDSTBL1XR1 AKAP7 SCARB2 224105_x_at TRDMT1 242413_at MAT2A LMBRD11557224_at GPR137 EFS 213601_at CDC42SE2 235138_at DCUN1D4 ZKSCAN5 RAB30INSIG2 228105_at POLK STX7 PRKAR2A 1562468_at AP1G2 CHAMP1 TBCC CCT2SATB1 236592_at SLC5A9 ASAH1 AK2 233107_at ZNF346 MNX1 215462_at TAF8216094_at ZNF765 ZFP90 236908_at 240046_at PAPOLG TRAPPC11 239933_x_atSPG11 E2F5 PTPLAD1 240500_at CSNK1A1 LOC283867 ALPK1 TRO 1559117_at200041_s_at PPP1R17 TNKS2 GOLGA2 MAN2A1 WARS2 233727_at 244383_at ZNF70PRRC2A TPM2 SMAD4 DPP8 CNOT2 239646_at 216704_at SH3BGRL ZNF500 MAP3K71569041_at NUFIP1 SMARCA2 FAM81A KCTD12 SBNO1 MBIP FOXK2 233626_at MAST4SRSF10 VPS13D 1555392_at PRPF18 236404_at FARSB SAT1 1556657_at RBM25DARS LOC100507918 HEATR3 1569538_at TMEM170A PRDX3 TLK2 NAP1L1200653_s_at REV1 ZFAND5 C1orf170 244219_at FOPNL SLC23A3 1558877_atUBE2G1 FOXP1-IT1 LOC145474 LINC00526 LIN52 212929_s_at 233664_at ERO1L235422_at ZMYND8 PDIA6 GRAMD4 1565743_at TMCO1 244341_at 240118_at242616_at CNST RAB5A SORL1 HERC4 242380_at ZNF419 NPY5R 243524_at BRI3BPMAST4 1565975_at SETDB2 STK38 ATG5 PAFAH1B2 TMED10 MAP1A XYLT2 ZFAND1DSERG1 TRIM32 TRIM2 TMEM50B PDXDC1 240216_at ZNF440 1567101_at 236114_atRABGAP1 1561128_at P2RX6 IL18BP PDK4 BAZ2A GFM1 244035_at 239876_atF2RL1 GOLGB1 FAM206A GLOD4 GRIK2 UBE2D2 CRLS1 PRRC2A ZC2HC1A NAPB HNMTUBE2W 238277_at ATM 239923_at HYMAI SP6 PLEKHB1 COL7A1 209084_s_atSWAP70 PRKAB1 220719_at 232400_at MAP3K2 240252_at 222197_s_at1559362_at 244156_at GJB1 TMX1 1557520_a_at LRBA EXOC5 227777_at UBA6MCFD2 216729_at 203742_s_at C12orf43 PEX3 FCF1 SLMAP ZCWPW1 235071_atHAX1 HDAC2 240241_at CNOT1 PRDX3 240520_at CRISPLD2 229679_at242343_x_at 242407_at CWC25 NDNL2 TNPO1 RCOR2 ULK4 CDC5L KIF5B RPS23ARF6 EIF3M GATC DCTN4 ETV5 235053_at 239661_at SNRPA1 BET1 TM6SF1232264_at ZNF565 1562600_at RBM8A HIPK2 ZFP3 ARL8B CCDC126 MAF EMX1 GNB4SRSF1 1562033_at VEZF1 ETNK1 USP46 ERO1L GDI2 NIN COPS8 PSMD11 KCNE3220728_at TERF2 1558093_s_at NSUN4 CNOT11 EDC3 MYOD1 ATM 236060_atLOC283887 MEGF9 ALG13 1561318_at 1558425_x_at 243895_x_at FBXO28 NNTNUDT7 FAM213A 234148_at 244548_at MRPL30 LUZP1 RAD1 1557688_at ITGA4VTA1 215278_at MFGE8 EIF3B 208810_at 1556658_a_at BRD2 243003_at ZNF12RPL35A RNF207 POLR3E UBXN2B FNTA KRAS UBE2B BHLHE40 BRD2 SLC33A1 ZKSCAN41555522_s_at MBD4 S1PR1 RECQL4 SUZ12 HSPH1 PEX2 BIRC3 233690_at PTPRCQRSL1 OR4D1 FOXP1 240665_at ZNF93 AURKAIP1 ARNTL 219112_at 238769_at235123_at BCR TLR1 LTBP4 FAM170A NIP7 242194_at 1559663_at 243203_atCENPT DDX51 STAM2 ARHGAP42 234942_s_at PRPF4B 1556352_at CANX PSMB4PQBP1 CHN2 239603_x_at TTYH2 208750_s_at NUDT13 221079_s_at 237013_atZBTB18 RAB40B 230350_at 232338_at GTF2H2 SERPINB9 SLC35A3 TBL1XR1234345_at SSR3 229469_at 244597_at 239166_at SPOP SNX6 RABEP1 244123_at230386_at TM9SF2 NKTR DCAF8 FCGR2C MED28 SH2B1 237185_at ATP2A2 ALG2242691_at ALDH3A2 234369_at 233824_at RNF130 OSGIN2 230761_at PLEKHG5CALU COG8 234113_at CCDC85C 225642_at 1569519_at DNM2 215528_at NEK4CNOT6L ND4 RASA1 SRSF4 CDV3 ARPC5 ERBB4 DHDH AKAP13 TMEM134 GINS4 ZBTB44DNAJC16 FABP6 NT5DC1 TTF2 ATF1 YTHDF2 SPOPL ZNF316 ATP5F1 E2F3 IBTK1562412_at FGD5-AS1 G3BP1 PMAIP1 239585_at PRKAR1A 242995_at ZNF44 KRCC1212241_at VANGL1 PSME3 TPD52 1568795_at FOXK2 HTR7 CYP21A2 PRKCB EML41555014_x_at MORF4L1 TRIM65 214223_at FAM27E3 PLA2G12A HADH FAM105B244791_at MTRF1L 244579_at CANX DPT TLN1 CXorf56 STARD4 XYLT2 240666_atGPSM3 220582_at TAGLN 207488_at 5-Mar PPP2R1B TP73-AS1 241692_at MRM1ZNF451 DAZAP2 232919_at 234488_s_at SLC35F5 ZNF503-AS2 LOC728537 CILP2SYNPO2 240529_at AKAP10 1569234_at MPZL2 TMEM168 ORAI2 ZNF566 USP34 MAOBABCA11P PDGFC SETD5-AS1 GTF3C5 R3HDM1 MTM1 CSNK1G3 MYCN BARD1 GSPT2 SMG6CASC4 CDRT15L2 ZNF747 ND4 SAMD4B PPP6R2 TMEM214 FAM204A TTF1 SLC5A8 TFR2MRPS10 LRCH3 IGSF9B 1566426_at R3HDM1 236615_at CREB1 TBC1D16 232759_atARV1 LYZ 1554771_at NUS1 IQSEC2 HSF2BP PRKD3 241769_at P2RY10 MOB1AMROH7 REST 1557538_at TRIM15 JKAMP C16orf55 CDK12 GLYR1 CACNA2D4232290_at DCAF16 ANKFY1 230868_at GPCPD1 C22orf43 AKAP13 CCDC50 FBXL5USP7 WDR45B SMCR8 GDE1 UBE3A PLA2G7 RFC3 231351_at 236931_at SLC30A5236322_at CNOT6L FAM22F 217679_x_at RAB1A 1565677_at EIF4G2 CUX1 MDM4PARP10 RAB9A PKN1 IPO13 1564378_a_at 242527_at RBBP5 YWHAE ACTR2 PON2215123_at TRIP6 233570_at DIS3 GRB10 1561834_a_at AKT2 HAUS6 1570229_atRAB1A NADKD1 COG6 SLC5A5 MRPL50 CRYL1 213740_s_at LRCH3 GLB1L3 XPO7240399_at KATNBL1 244019_at LOC100132874 TMEM64 AP1S2 231934_at PRSS3P21570439_at RAB7L1 MARS2 NFKBIB TRAK1 1-Sep CACNA2D4 ORC5 RP2 MLL2240238_at 1561195_at GLUD2 RNF170 TGIF1 NOC4L 230630_at LIN7C BLOC1S6MS4A6A PPARA RPRD1A RBM22 240319_at LINC00667 SLC35D2 233313_at RBM7PIGM 237575_at SCAMP1 TUBD1 SMARCA4 TTLL9 TSHZ2 S100A14 MAP3K2 SPOPL ADOATPBD4 SPAG9 ARL6IP6 IBA57 SWAP70 ADH5 VASN SPPL3 ARF4 ZCCHC10 HIBADH243673_at KLC4 ABCB8 P4HA2 1560741_at TIMM17A MOSPD2 ABHD5 COL17A11559702_at FAM43A DOCK4 TAF9B 214740_at 230617_at TMEM106B ALS2CR8C9orf53 YY1 PTP4A2 HADH 236125_at 233296_x_at 1569311_at MESDC1 EPC1ATF6 WTAP INF2 231005_at GON4L MZT2B 1554089_s_at IGDCC3 PRMT6 C1orf228LARP4 RASGRF1 PLEKHA4 ENDOV IL13RA1 NLGN3 217446_x_at SETD5-AS1200624_s_at 1556931_at SF3A2 1558670_at RPP14 220691_at TMEM239 TP53AIP1FBXL12 PAGR1 RNF113A FBXL20 237171_at 242233_at 233554_at RRAS21558237_x_at SQSTM1 MANEA SPEN 241867_at PDE8A PTGER1 TRAPPC10 CLDN16C16orf52 OGFRL1 IFNGR1 1561871_at 1565762_at LOC100506369 SYMPK231191_at UHRF1BP1L LCMT2 STX17 C6orf120 ITSN2 1560082_at 240446_atMED23 PRKRA ANKRD13C RFC1 RHEB GBAS 215874_at MED30 1556373_a_at ZC3H7ARHOA MRPS10 TRAM1 RHAG 214658_at TRIM23 RRN3P3 ANKS1B ATPAF1 PPID1561167_at 240505_at ELMSAN1 202374_s_at NR2C2 APP 235804_at DYRK1BADAT1 244382_at ZNF451 RERE ASPHD1 TOR1AIP1 242320_at MCL1 DENND6B1560862_at PTEN TMEM203 1555194_at ZNF430 FAM83G 239453_at CCNT2 ASAH1238108_at SFRP1 NAA40 ERN2 237264_at CUL4B 240315_at ARNT TSPAN3240279_at ERICH1 BZRAP1-AS1 SCOC ZNF318 MLLT10 222371_at TTC9C MTSS1C15orf57 ZFR WDR4 PEBP1 IGIP DNM1 1562067_at MEF2A 234609_at AP1G1MGC70870 ZNF777 235862_at COL7A1 NIPSNAP3A TRIOBP JAK2 RALGAPA1 UBE2D4ZNRF2 ANKRD52 RNF213 221242_at IMPAD1 240478_at RFC3 TMEM251 HNRNPMLUC7L 222315_at 239409_at FAM110A 234590_x_at EBPL 217549_at CDKN2AIPZNF146 CRYBB3 NAP1L1 LOC142937 LOC283174 SCAMP1 RASA2 WDR19 242920_atCEP57 234788_x_at 216584_at CSNK1A1 RPS2 FAM103A1 239121_at UBE2D1238656_at B3GNT2 ETNK1 STK38L C6orf106 PTP4A1 CAT LOC100131067 227505_atDNAJC10 PPP1R12C SMG5 LOC100505876 FDX1 C2orf43 ARFIP2 ETNK1 CEP350 EMC4TADA3 NCK1 DEFB1 IRF8 233473_x_at GNAQ IKZF1 222378_at TMEM55A TMEM88LMBRD2 228694_at 242827_x_at ST3GAL6 SSBP2 ENOPH1 LOC100505555 RICTORDNAJC14 222375_at MAU2 CEP68 FAM45A NDFIP2 NEDD9 ENTHD2 OSBPL11 WDR48HBP1 243473_at CD47 1557270_at PAFAH1B2 PRKCH PSME3 ACYP2 GSDMB TPGS1NOL9 ZNF224 HNRNPUL1 MTFR1 FABP3 PHLPP2 POU5F1P4 EPN1 244732_at TRIM4DR1 CYP2A6 CHD9 TET3 LILRA2 ZAN FLT1 ADAM10 HEATR5B PPP1R8 ANTXR1215986_at PCBD2 202397_at RPRD1A USP9X PATL1 SRP72 1562194_at MAVSCAMSAP1 CCL22 1562056_at KLF4 TRAPPC1 PDIA6 1570408_at WDR77 236438_atDRAXIN PRKCB 234201_x_at 239171_at FAM175A C9orf84 NUP188 APCDD1L-AS1VAMP2 CCDC108 INPP4A OTOR VAV3 243249_at 239264_at NLRP1 HIGD1A MRPL19YWHAZ CHMP2B IFNGR2 240165_at MAVS TGFB1 CHD9 ABCD3 HNRNPC 1554413_s_atLYPLA1 ARHGAP27 233960_s_at DSTYK ARHGAP26 1566959_at FAM99B FAM131C228812_at CASP8 243568_at GGT7 CAT SETBP1 DLD CUL4A ATXN10 RPS15 RPS10P7ELP2 SMIM15 233648_at FOXP3 1565915_at ETNK1 1557512_at 1557724_a_at242558_at 217164_at 232584_at SUMO2 TRMT11 NSMCE4A 1557562_at222436_s_at 233783_at TCOF1 227556_at ENDOG MAGI1 DNAJB11 SNX5 POGZST8SIA4 EVI5L UBTD2 237311_at 241460_at RHOQ PDLIM5 FAM63A EIF4E3 SMAD7MUC3B DKFZP547L112 BTBD7 IL1R2 CFDP1 204347_at 1556336_at EDIL3239613_at MCTP2 COG7 UCHL5 MOGAT2 GHDC UFL1 238807_at 234645_at MGA242476_at COMMD10 POLH CADM1 243992_at CSGALNACT1 NICN1 HAP1 237456_atOBSCN VAMP3 NR5A1 DDX42 TMEM48 229717_at HNRNPH1 243280_at SEC23ADNAJC10 ANKZF1 C17orf70 NUCKS1 NAA50 C15orf57 1555996_s_at MUC201566823_a_at USP31 AVL9 SRSF5 241445_at DDR1-AS1 CTSC HDGFRP2 TOB2CTDSPL2 240008_at RCN2 244674_at XRCC6 C5orf22 6-Mar PRKAR1B 240538_at242058_at 226252_at 236612_at PCBP3 LOC374443 CHD2 INTS10 OXR11558710_at 1556055_at 231604_at FASTKD3 SARDH ALPI RAB3IP SPTLC2 B4GALT1243286_at SCLT1 CASP8 KLHL28 PCMT1 EXOC6 CPNE6 229575_at 6-Sep CCNG2TMTC4 LYRM4 ZDHHC13 NKTR 232626_at ZNF207 SGK494 ERVK13-1 CD44 SEZ6L2211910_at PEBP1 243860_at NECAP2 RECQL5 ACHE DNASE1L3 ROCK1 244332_atCCDC40 SULT4A1 TTC9B NF1 242927_at MBD2 237330_at SEC23IP SUPT6H TTC5241387_at 235613_at KIAA0141 ID2 GPR65 222156_x_at ZNF304 LOC100507283FAM174A LINC00661 FLNB FAM181A 227384_s_at RAB22A 241376_at DRD2 ATP9AATF1 HOXB2 PDCD6IP TOR3A FYTTD1 PLAGL1 RHOQ AGGF1 9-Sep 243454_at MAPRE1LRRC37A3 IL13RA1 HIPK3 ZBTB43 SLC20A2 POT1 LINS CCNY SERP1 235847_atZNF655 242768_at 1566166_at CEP57 TOR1AIP1 VAMP4 KCP 243078_at RICTORLOC729852 242824_at 1556865_at ZNF398 RSBN1L DCAF13 PEX3 IKBIP GOLPH3BCLAF1 UPRT GRB10 ARID4B CHD7 PEX12 243396_at C19orf43 1560982_at DCAF8SETD5 KBTBD4 216567_at PTER PTP4A2 1557422_at REPS1 MLLT11 COX2239780_at 1562051_at SNAP23 233323_at FLJ31813 ATP11A 239451_at CCNL1OGFRL1 TTBK2 RAB6A 216448_at CADM3 C17orf58 239164_at 237895_at PTPN11PDIA6 243512_x_at GRIPAP1 207756_at IL13RA1 CHMP1B UBE2D3 ZBTB20 SIAH1242732_at DIO3 242457_at APPBP2 239555_at 232788_at NOL9 234091_atCIAPIN1 MCTP2 236610_at RTCA TCF3 233228_at PRRC2A ZNF708 MIER3 NENFSAE1 B4GALNT3 240775_at MIA SLC32A1 233922_at ZNF280D MED30 238712_atXRCC1 215197_at 216850_at C12orf5 243826_at 216465_at RBM15 ADD1 SNX101556327_a_at TANK ZFYVE21 PDP1 230980_x_at BTBD18 MMP28 232909_s_atRQCD1 CD9 FAM184B BRCC3 ACSS1 TNFRSF19 SCAF11 COMMD10 FGF18 FAHD2CP ULK21569999_at 1562063_x_at KCTD18 234449_at 243509_at AGO1 1558418_atSMARCC2 HEATR5A LACTB2 TFB2M TPI1 UBE4B XAB2 NCOA2 1556778_at PALM3TPSAB1 239557_at SLC25A40 TRAPPC13 KDELR2 242386_x_at CCDC28A 232890_atTSPAN12 CELF2 238559_at ATP5S CEP152 ITPR1 KLHL9 206088_at ZNF235 DCTDCDK9 MEAF6 217653_x_at PRSS53 239619_at REL RAE1 239331_at U2SURPSLC22A31 GNL3L TOMM22 PROK2 243808_at RALGAPA2 POLK 216656_at SSTR5-AS1CCNY ZNF580 TPM3 SEC24A USP2 LRMP 233790_at KLF9 244474_at MFI2 CGGBP1TTL PLEKHG3 241788_x_at LOC150381 RUNX1-IT1 SOAT1 ALG13 G3BP2204006_s_at3-class univariate F-test was done on the Discovery cohort (1000 randompermutations and FDR <10%; BRB ArrayTools)Number of significant genes by controlling the proportion of falsepositive genes: 2977Sorted by p-value of the univariate test.

Class 1: ADNR; Class 2: AR; Class 3: TX.

With probability of 80% the first 2977 genes contain no more than 10% offalse discoveries. Further extension of the list was halted because thelist would contain more than 100 false discoveriesThe ‘Pairwise significant’ column shows pairs of classes withsignificantly different gene expression at alpha =0.01. Class labels ina pair are ordered (ascending) by their averaged gene expression.

TABLE 1c The top 200 gene probeset used in the 3-Way AR, ADNR TX ANOVAAnalysis (using the HT HG-U133 + PM Array Plates) 3-Way AR, ADNR TXANOVA Analysis p-value - # Probeset ID Gene Symbol Gene Title phenotype1 213718_PM_at RBM4 RNA binding motif protein 4 5.39E−10 2227878_PM_s_at ALKBH7 alkB, alkylation repair homolog 7 (E. 3.15E−08coli) 3 214405_PM_at 214405_PM_at_EST1 EST1 3.83E−08 4 210792_PM_x_atSIVA1 SIVA1, apoptosis-inducing factor 4.64E−08 5 214182_PM_at214182_PM_at_EST2 EST2 6.38E−08 6 1554015_PM_a_at CHD2 chromodomainhelicase DNA binding 6.50E−08 protein 2 7 225839_PM_at RBM33 RNA bindingmotif protein 33 7.41E−08 8 1554014_PM_at CHD2 chromodomain helicase DNAbinding 7.41E−08 protein 2 9 214263_PM_x_at POLR2C polymerase (RNA) II(DNA directed) 7.47E−08 polypeptide C, 33 kDa 10 1556865_PM_at1556865_PM_at_EST3 EST3 9.17E−08 11 203577_PM_at GTF2H4 generaltranscription factor IIH, 9.44E−08 polypeptide 4, 52 kDa 12 218861_PM_atRNF25 ring finger protein 25 1.45E−07 13 206061_PM_s_at DICER1 dicer 1,ribonuclease type III 1.53E−07 14 225377_PM_at C9orf86 chromosome 9 openreading frame 86 1.58E−07 15 1553107_PM_s_at C5orf24 chromosome 5 openreading frame 24 2.15E−07 16 1557246_PM_at KIDINS220 kinaseD-interacting substrate, 220 kDa 2.43E−07 17 224455_PM_s_at ADPGKADP-dependent glucokinase 3.02E−07 18 201055_PM_s_at HNRNPA0heterogeneous nuclear ribonucleoprotein 3.07E−07 A0 19 236237_PM_at236237_PM_at_EST4 EST4 3.30E−07 20 211833_PM_s_at BAX BCL2-associated Xprotein 3.92E−07 21 1558111_PM_at MBNL1 muscleblind-like (Drosophila)3.93E−07 22 206113_PM_s_at RAB5A RAB5A, member RAS oncogene family3.95E−07 23 202306_PM_at POLR2G polymerase (RNA) II (DNA directed)4.83E−07 polypeptide G 24 242268_PM_at CELF2 CUGBP, Elav-like familymember 2 5.87E−07 25 223332_PM_x_at RNF126 ring finger protein 1266.24E−07 26 1561909_PM_at 1561909_PM_at_EST5 EST5 6.36E−07 27213940_PM_s_at FNBP1 formin binding protein 1 6.60E−07 28 210655_PM_s_atFOXO3 /// FOXO3B forkhead box O3 /// forkhead box O3B 6.82E−07pseudogene 29 233303_PM_at 233303_PM_at_EST6 EST6 8.00E−07 30244219_PM_at 244219_PM_at_EST7 EST7 9.55E−07 31 35156_PM_at R3HCC1 R3Hdomain and coiled-coil containing 1.02E−06 1 32 215210_PM_s_at DLSTdihydrolipoamide S-succinyltransferase 1.08E−06 (E2 component of2-oxo-glutarate complex) 33 1563431_PM_x_at CALM3 Calmodulin 3(phosphorylase kinase, 1.15E−06 delta) 34 202858_PM_at U2AF1 U2 smallnuclear RNA auxiliary factor 1 1.18E−06 35 1555536_PM_at ANTXR2 anthraxtoxin receptor 2 1.21E−06 36 210313_PM_at LILRA4 leukocyteimmunoglobulin-like receptor, 1.22E−06 subfamily A (with TM domain),member 4 37 216997_PM_x_at TLE4 transducin-like enhancer of split 41.26E−06 (E(sp1) homolog, Drosophila) 38 201072_PM_s_at SMARCC1 SWI/SNFrelated, matrix associated, 1.28E−06 actin dependent regulator ofchromatin, subfamily c 39 223023_PM_at BET1L blocked early in transport1 homolog (S. 1.36E−06 cerevisiae)-like 40 201556_PM_s_at VAMP2vesicle-associated membrane protein 2 1.39E−06 (synaptobrevin 2) 41218385_PM_at MRPS18A mitochondrial ribosomal protein S18A 1.44E−06 421555420_PM_a_at KLF7 Kruppel-like factor 7 (ubiquitous) 1.50E−06 43242726_PM_at 242726_PM_at_EST8 EST8 1.72E−06 44 233595_PM_at USP34ubiquitin specific peptidase 34 1.79E−06 45 218218_PM_at APPL2 adaptorprotein, phosphotyrosine 1.80E−06 interaction, PH domain and leucinezipper containing 2 46 240991_PM_at 240991_PM_at_EST9 EST9 1.95E−06 47210763_PM_x_at NCR3 natural cytotoxicity triggering receptor 3 2.05E−0648 201009_PM_s_at TXNIP thioredoxin interacting protein 2.10E−06 49221855_PM_at SDHAF1 succinate dehydrogenase complex 2.19E−06 assemblyfactor 1 50 241955_PM_at HECTD1 HECT domain containing 1 2.37E−06 51213872_PM_at C6orf62 Chromosome 6 open reading frame 62 2.57E−06 52243751_PM_at 243751_PM_at_EST10 EST10 2.60E−06 53 232908_PM_at ATAD2BATPase family, AAA domain containing 2.64E−06 2B 54 222413_PM_s_at MLL3myeloid/lymphoid or mixed-lineage 2.74E−06 leukemia 3 55 217550_PM_atATF6 activating transcription factor 6 2.86E−06 56 223123_PM_s_atC1orf128 chromosome 1 open reading frame 128 2.87E−06 57 202283_PM_atSERPINF1 serpin peptidase inhibitor, clade F 2.87E−06 (alpha-2antiplasmin, pigment epithelium derived fa 58 200813_PM_s_at PAFAH1B1platelet-activating factor acetylhydrolase 3.10E−06 1b, regulatorysubunit 1 (45 kDa) 59 223312_PM_at C2orf7 chromosome 2 open readingframe 7 3.30E−06 60 217399_PM_s_at FOXO3 /// FOXO3B forkhead box O3 ///forkhead box O3B 3.31E−06 pseudogene 61 218571_PM_s_at CHMP4A chromatinmodifying protein 4A 3.47E−06 62 228727_PM_at ANXA11 annexin A113.73E−06 63 200055_PM_at TAF10 TAF10 RNA polymerase II, TATA box3.76E−06 binding protein (TBP)-associated factor, 30 kDa 64242854_PM_x_at DLEU2 deleted in lymphocytic leukemia 2 (non- 3.84E−06protein coding) 65 1562250_PM_at 1562250_PM_at_EST11 EST11 3.99E−06 66208657_PM_s_at SEPT9 septin 9 4.12E−06 67 201394_PM_s_at RBM5 RNAbinding motif protein 5 4.33E−06 68 200898_PM_s_at MGEA5 meningiomaexpressed antigen 5 4.55E−06 (hyaluronidase) 69 202871_PM_at TRAF4 TNFreceptor-associated factor 4 4.83E−06 70 1558527_PM_at LOC100132707hypothetical LOC100132707 4.85E−06 71 203479_PM_s_at OTUD4 OTU domaincontaining 4 4.86E−06 72 219931_PM_s_at KLHL12 kelch-like 12(Drosophila) 4.88E−06 73 203496_PM_s_at MED1 mediator complex subunit 14.99E−06 74 216112_PM_at 216112_PM_at_EST12 EST12 5.22E−06 751557418_PM_at ACSL4 Acyl-CoA synthetase long-chain family 5.43E−06member 4 76 212113_PM_at ATXN7L3B ataxin 7-like 3B 5.67E−06 77204246_PM_s_at DCTN3 dynactin 3 (p22) 5.68E−06 78 235868_PM_at MGEA5Meningioma expressed antigen 5 5.70E−06 (hyaluronidase) 79232725_PM_s_at MS4A6A membrane-spanning 4-domains, 5.73E−06 subfamily A,member 6A 80 212886_PM_at CCDC69 coiled-coil domain containing 695.84E−06 81 226840_PM_at H2AFY H2A histone family, member Y 5.86E−06 82226825_PM_s_at TMEM165 transmembrane protein 165 5.96E−06 83227924_PM_at INO80D INO80 complex subunit D 6.18E−06 84 238816_PM_atPSEN1 presenilin 1 6.18E−06 85 224798_PM_s_at C15orf17 chromosome 15open reading frame 17 6.31E−06 86 243295_PM_at RBM27 RNA binding motifprotein 27 6.34E−06 87 207460_PM_at GZMM granzyme M (lymphocytemet-ase 1) 6.46E−06 88 242131_PM_at ATP6 ATP synthase F0 subunit 66.56E−06 89 228637_PM_at ZDHHC1 zinc finger, DHHC-type containing 16.80E−06 90 233575_PM_s_at TLE4 transducin-like enhancer of split 47.08E−06 (E(sp1) homolog, Drosophila) 91 215088_PM_s_at SDHC succinatedehydrogenase complex, 7.18E−06 subunit C, integral membrane protein, 15kDa 92 209675_PM_s_at HNRNPUL1 heterogeneous nuclear ribonucleoprotein7.35E−06 U-like 1 93 37462_PM_i_at SF3A2 splicing factor 3a, subunit 2,66 kDa 7.38E−06 94 236545_PM_at 236545_PM_at_EST13 EST13 7.42E−06 95232846_PM_s_at CDH23 cadherin-related 23 7.42E−06 96 242679_PM_atLOC100506866 hypothetical LOC100506866 7.52E−06 97 229860_PM_x_atC4orf48 chromosome 4 open reading frame 48 7.60E−06 98 243557_PM_at243557_PM_at_EST14 EST14 7.62E−06 99 222638_PM_s_at C6orf35 chromosome 6open reading frame 35 7.67E−06 100 209477_PM_at EMD emerin 7.70E−06 101213328_PM_at NEK1 NIMA (never in mitosis gene a)-related 7.72E−06 kinase1 102 1555843_PM_at HNRNPM Heterogeneous nuclear ribonucleoprotein7.72E−06 M 103 241240_PM_at 241240_PM_at_EST15 EST15 7.74E−06 104218600_PM_at LIMD2 LIM domain containing 2 7.81E−06 105 212994_PM_atTHOC2 THO complex 2 7.84E−06 106 243046_PM_at 243046_PM_at_EST16 EST168.03E−06 107 211947_PM_s_at BAT2L2 HLA-B associated transcript 2-like 28.04E−06 108 238800_PM_s_at ZCCHC6 Zinc finger, CCHC domain containing 68.09E−06 109 228723_PM_at 228723_PM_at_EST17 EST17 8.11E−06 110242695_PM_at 242695_PM_at_EST18 EST18 8.30E−06 111 216971_PM_s_at PLECplectin 8.39E−06 112 220746_PM_s_at UIMC1 ubiquitin interaction motifcontaining 1 8.44E−06 113 238840_PM_at LRRFIP1 leucine rich repeat (inFLU) interacting 8.59E−06 protein 1 114 1556055_PM_at1556055_PM_at_EST19 EST19 8.74E−06 115 AFFX- AFFX- EST20 9.08E−06M27830_5_at M27830_5_at_EST20 116 215248_PM_at GRB10 growth factorreceptor-bound protein 10 9.43E−06 117 211192_PM_s_at CD84 CD84 molecule1.01E−05 118 214383_PM_x_at KLHDC3 kelch domain containing 3 1.04E−05119 208478_PM_s_at BAX BCL2-associated X protein 1.08E−05 120229422_PM_at NRD1 nardilysin (N-arginine dibasic 1.12E−05 convertase)121 206636_PM_at RASA2 RAS p21 protein activator 2 1.14E−05 1221559589_PM_a_at 1559589_PM_a_at_EST21 EST21 1.16E−05 123 229676_PM_atMTPAP Mitochondrial poly(A) polymerase 1.18E−05 124 201369_PM_s_atZFP36L2 zinc finger protein 36, C3H type-like 2 1.19E−05 125215535_PM_s_at AGPAT1 1-acylglycerol-3-phosphate O- 1.25E−05acyltransferase 1 (lysophosphatidic acid acyltransferase, 126212162_PM_at KIDINS220 kinase D-interacting substrate, 220 kDa 1.26E−05127 218893_PM_at ISOC2 isochorismatase domain containing 2 1.26E−05 128204334_PM_at KLF7 Kruppel-like factor 7 (ubiquitous) 1.26E−05 129221598_PM_s_at MED27 mediator complex subunit 27 1.31E−05 130221060_PM_s_at TLR4 toll-like receptor 4 1.32E−05 131 224821_PM_atABHD14B abhydrolase domain containing 14B 1.35E−05 132 244349_PM_at244349_PM_at_EST22 EST22 1.38E−05 133 244418_PM_at 244418_PM_at_EST23EST23 1.41E−05 134 225157_PM_at MLXIP MLX interacting protein 1.42E−05135 228469_PM_at PPID Peptidylprolyl isomerase D 1.44E−05 136224332_PM_s_at MRPL43 mitochondrial ribosomal protein L43 1.47E−05 1371553588_PM_at ND3 /// SH3KBP1 NADH dehydrogenase, subunit 3 1.48E−05(complex I) /// SH3-domain kinase binding protein 1 138 238468_PM_atTNRC6B trinucleotide repeat containing 6B 1.49E−05 139 235727_PM_atKLHL28 kelch-like 28 (Drosophila) 1.53E−05 140 218978_PM_s_at SLC25A37solute carrier family 25, member 37 1.59E−05 141 221214_PM_s_at NELFnasal embryonic LHRH factor 1.62E−05 142 204282_PM_s_at FARS2phenylalanyl-tRNA synthetase 2, 1.64E−05 mitochondrial 143 236155_PM_atZCCHC6 Zinc finger, CCHC domain containing 6 1.65E−05 144 224806_PM_atTRIM25 tripartite motif-containing 25 1.66E−05 145 202840_PM_at TAF15TAF15 RNA polymerase II, TATA box 1.67E−05 binding protein(TBP)-associated factor, 68 kDa 146 207339_PM_s_at LTB lymphotoxin beta(TNF superfamily, 1.68E−05 member 3) 147 221995_PM_s_at221995_PM_s_at_EST24 EST24 1.69E−05 148 242903_PM_at IFNGR1 interferongamma receptor 1 1.70E−05 149 228826_PM_at 228826_PM_at_EST25 EST251.70E−05 150 220231_PM_at C7orf16 chromosome 7 open reading frame 161.71E−05 151 242861_PM_at NEDD9 neural precursor cell expressed,1.72E−05 developmentally down-regulated 9 152 202785_PM_at NDUFA7 NADHdehydrogenase (ubiquinone) 1 1.74E−05 alpha subcomplex, 7, 14.5 kDa 153205787_PM_x_at ZC3H11A zinc finger CCCH-type containing 11A 1.76E−05 1541554333_PM_at DNAJA4 DnaJ (Hsp40) homolog, subfamily A, 1.77E−05 member4 155 1563315_PM_s_at ERICH1 glutamate-rich 1 1.82E−05 156202101_PM_s_at RALB v-ral simian leukemia viral oncogene 1.82E−05homolog B (ras related; GTP binding protein) 157 210210_PM_at MPZL1myelin protein zero-like 1 1.84E−05 158 217234_PM_s_at EZR ezrin1.85E−05 159 219222_PM_at RBKS ribokinase 1.86E−05 160 213161_PM_atTMOD1 /// TSTD2 tropomodulin 1 /// thiosulfate 1.88E−05sulfurtransferase (rhodanese)-like domain containing 2 161 236497_PM_atLOC729683 hypothetical protein LOC729683 1.91E−05 162 203111_PM_s_atPTK2B PTK2B protein tyrosine kinase 2 beta 1.93E−05 163 1554571_PM_atAPBB1IP amyloid beta (A4) precursor protein- 1.97E−05 binding, family B,member 1 interacting protein 164 212007_PM_at UBXN4 UBX domain protein 41.98E−05 165 1569106_PM_s_at SETD5 SET domain containing 5 1.98E−05 166243032_PM_at 243032_PM_at_EST26 EST26 2.00E−05 167 216380_PM_x_at216380_PM_x_at_EST27 EST27 2.00E−05 168 217958_PM_at TRAPPC4 traffickingprotein particle complex 4 2.10E−05 169 200884_PM_at CKB creatinekinase, brain 2.11E−05 170 208852_PM_s_at CANX calnexin 2.12E−05 1711558624_PM_at 1558624_PM_at_EST28 EST28 2.19E−05 172 203489_PM_at SIVA1SIVA1, apoptosis-inducing factor 2.23E−05 173 240652_PM_at240652_PM_at_EST29 EST29 2.25E−05 174 214639_PM_s_at HOXA1 homeobox A12.37E−05 175 203257_PM_s_at C11orf49 chromosome 11 open reading frame 492.45E−05 176 217507_PM_at SLC11A1 solute carrier family 11(proton-coupled 2.56E−05 divalent metal ion transporters), member 1 177223166_PM_x_at C9orf86 chromosome 9 open reading frame 86 2.57E−05 178206245_PM_s_at IVNS1ABP influenza virus NS1A binding protein 2.62E−05179 223290_PM_at PDXP /// SH3BP1 pyridoxal (pyridoxine, vitamin B6)2.64E−05 phosphatase /// SH3-domain binding protein 1 180 224732_PM_atCHTF8 CTF8, chromosome transmission fidelity 2.69E−05 factor 8 homolog(S. cerevisiae) 181 204560_PM_at FKBP5 FK506 binding protein 5 2.75E−05182 1556283_PM_s_at FGFR1OP2 FGFR1 oncogene partner 2 2.75E−05 183212451_PM_at SECISBP2L SECIS binding protein 2-like 2.76E−05 184208750_PM_s_at ARF1 ADP-ribosylation factor 1 2.81E−05 185 238987_PM_atB4GALT1 UDP-Gal:betaGlcNAc beta 1,4- 2.82E−05 galactosyltransferase,polypeptide 1 186 227211_PM_at PHF19 PHD finger protein 19 2.84E−05 187223960_PM_s_at C16orf5 chromosome 16 open reading frame 5 2.86E−05 188223009_PM_at C11orf59 chromosome 11 open reading frame 59 2.88E−05 189229713_PM_at PIP4K2A Phosphatidylinositol-5-phosphate 4- 2.96E−05kinase, type II, alpha 190 1555330_PM_at GCLC glutamate-cysteine ligase,catalytic 2.96E−05 subunit 191 242288_PM_s_at EMILIN2 elastinmicrofibril interfacer 2 2.97E−05 192 207492_PM_at NGLY1 N-glycanase 12.98E−05 193 233292_PM_s_at ANKHD1 /// ANKHD1- ankyrin repeat and KHdomain 3.00E−05 EIF4EBP3 containing 1 /// ANKHD1-EIF4EBP3 readthrough194 1569600_PM_at DLEU2 Deleted in lymphocytic leukemia 2 (non- 3.00E−05protein coding) 195 218387_PM_s_at PGLS 6-phosphogluconolactonase3.03E−05 196 239660_PM_at RALGAPA2 Ral GTPase activating protein, alpha3.07E−05 subunit 2 (catalytic) 197 230733_PM_at 230733_PM_at_EST30 EST303.07E−05 198 1557804_PM_at 1557804_PM_at_EST31 EST31 3.11E−05 199210969_PM_at PKN2 protein kinase N2 3.12E−05 200 233937_PM_at GGNBP2gametogenetin binding protein 2 3.13E−05

TABLE 1d The gene list of all 4132 genes analyzed in the 3-Way AR, ADNRTX ANOVA Analysis (using the HT HG-U133 + PM Array Plates) RBM4 IFT52TMSB4Y SLAMF6 ALKBH7 240759_PM_at FLT3LG LOC100287401 214405_PM_at_EST1EZR MTMR2 CFLAR SIVA1 DEXI C8orf82 PIGV 214182_PM_at_EST2 SMARCC2 ELP4RB1CC1 CHD2 ADPGK TEX264 SLC25A37 RBM33 TACR1 STX3 CES2 CHD2 FLJ10661208278_PM_s_at CLYBL POLR2C ACSL4 VPS25 SDHD 1556865_PM_at_EST3 STAC3ISCA1 TRAF3IP3 GTF2H4 PHF20 SNCA MRPL2 RNF25 UBXN7 CCDC17 CISH DICER1SCFD1 CCDC51 FBXO9 C9orf86 1559391_PM_s_at MTPAP LOC284454 C5orf241570151_PM_at 1566958_PM_at CUL4B KIDINS220 MED13L STRADB PHTF1 ADPGKXCL1 HEY1 TACC1 HNRNPA0 1563833_PM_at LOC100129361 RBM8A236237_PM_at_EST4 CPEB4 EHBP1 PCMT1 BAX PFKFB2 TMEM223 LOC100507315 ///PPP2R5C MBNL1 244357_PM_at OGT TNPO1 RAB5A DAAM2 231005_PM_at FXR2POLR2G CCDC57 C16orf5 SLC25A19 CELF2 CDKN1C GNGT2 RAB34 RNF126 CTAGE5IKZF1 IFT52 1561909_PM_at_EST5 PTEN /// PTENP1 242008_PM_at LCP1 FNBP1DENND1C STON2 ZNF677 FOXO3 /// FOXO3B TAGAP CALM1 HCG22233303_PM_at_EST6 ANXA3 217347_PM_at 1564155_PM_x_at 244219_PM_at_EST7UBE2I ID3 OBFC2B R3HCC1 LOC728723 TGIF2 SRSF2 DLST 243695_PM_at CELF2SORBS1 CALM3 C15orf57 MAP3K13 WDR82 U2AF1 ALG8 SLC4A7 ALOX5 ANTXR2 PSMD7CCDC97 TES LILRA4 C9orf84 UBE2J2 CD46 TLE4 TSPAN18 PATL1 CALU SMARCC1PIAS2 DTX3 ARHGDIA BET1L RNF145 ZNF304 SDCCAG8 VAMP2 C19orf43 GDAP2C7orf26 MRPS18A 208324_PM_at 227729_PM_at SEMA4F KLF7 FCAR TMED2 TLR1242726_PM_at_EST8 LTN1 C6orf125 222300_PM_at USP34 PKN2 PGAP3 NUDT4APPL2 TTLL3 ZMYND11 MLL3 240991_PM_at_EST9 NAA15 CYB5B SVIL NCR3 GNAQMBD6 IFT46 TXNIP KLF6 241434_PM_at ARIH2 SDHAF1 TOR1A GHITM NFYC HECTD1SLC27A5 244555_PM_at RABL2A /// RABL2B C6orf62 SLC2A11 CTSB 235661_PM_at243751_PM_at_EST10 240939_PM_x_at LYRM7 PTPRC ATAD2B LSMD1 DNASE1243031_PM_at MLL3 WAC FECH ESD ATF6 LOC100128590 UBE2W 244642_PM_atC1orf128 NSMAF TLK1 EDF1 SERPINF1 VAV3 229483_PM_at C1orf159 PAFAH1B1239166_PM_at MKLN1 CHP C2orf7 ST8SIA4 DERL2 1558385_PM_at FOXO3 ///FOXO3B ACTR10 C11orf31 1557667_PM_at CHMP4A PXK PHC3 PDE6B ANXA11 DGUOKPPM1L MFAP1 TAF10 MTG1 LOC100505764 TMED4 DLEU2 1557538_PM_at ZNF281PPARA 1562250_PM_at_EST11 TXNL4B JAK3 ARHGAP24 SEPT9 239723_PM_at NME6PSMD9 RBM5 MAPKAPK2 ZMAT3 LOC100507006 MGEA5 CXXC5 MADD GLUD1 TRAF4CHST2 SLC2A8 CCDC19 LOC100132707 GSTM1 PALLD AKR7A3 OTUD4 210824_PM_atLOC283392 SCLY KLHL12 ADAM17 243105_PM_at GCLC MED1 244267_PM_at FCARUSP32 216112_PM_at_EST12 LOC339862 CNOT8 GSPT1 ACSL4 1561733_PM_at1555373_PM_at RUFY2 ATXN7L3B PEX14 HNRNPL RHEBL1 DCTN3 EXOSC1 LOC646014FAF1 MGEA5 230354_PM_at VAV3 WSB1 MS4A6A ZBTB43 LRBA DNAJC7 CCDC69C8orf60 HGD MGC16275 H2AFY C15orf54 TADA2B 1556508_PM_s_at TMEM165 CELF2227223_PM_at VAPA INO80D 241722_PM_x_at SLC25A33 RRAGC PSEN1 LIMD1LMBR1L TDG C15orf17 OTUB1 SPRED1 ENO1 RBM27 KCTD10 SEC24D SIDT2 GZMM1560443_PM_at 239848_PM_at UMPS ATP6 FBXO41 232867_PM_at FLJ35816 ZDHHC1DCAF6 /// ND4 SLC35C2 RPRD1A TLE4 PEPD ANGEL1 PPIB SDHC CERK242320_PM_at ALAD HNRNPUL1 PTPMT1 DSTYK C10orf54 SF3A2 FAM189B ERGIC2C16orf88 236545_PM_at_EST13 TMED1 QRSL1 EPB41 CDH23 UBL4A KDM2A PRDX2LOC100506866 SLC25A37 MED6 MMAB C4orf48 SUGT1 ATP5D PRDX3243557_PM_at_EST14 MPZL1 RNF130 FOXO3 C6orf35 GSTM2 TCOF1 1560798_PM_atEMD MLL5 ARHGEF2 ENY2 NEK1 C19orf25 REPIN1 PTEN HNRNPM C6orf108 MRPS27ZNF879 241240_PM_at_EST15 FPR2 RNASET2 233223_PM_at LIMD2 EXOSC9 C9orf70AGAP10 /// AGAP4 /// AGAP9 /// BMS1P1 /// BMS1P5 /// LOC399753 THOC2BMS1P1 ///BMS1P5 243671_PM_at NUBPL 243046_PM_at_EST16 CCDC92 GFM2C20orf4 BAT2L2 HIF1AN ZNHIT1 BBS2 ZCCHC6 IDS ZNF554 BTBD19228723_PM_at_EST17 240108_PM_at KIAA1609 230987_PM_at 242695_PM_at_EST18BMP6 ZNF333 C1QBP PLEC AKR1B1 CD59 HIST1H3B UIMC1 NDUFB8 UBXN1 C16orf88LRRFIP1 CCDC69 LZTFL1 RANGRF 1556055_PM_at_EST19 LRRFIP2 C17orf79 DSTNAFFX- OXR1 PSMG2 ADCY3 M27830_5_at_EST20 GRB10 MED20 LOC151146 SSH2 CD84ADNP2 C15orf26 ATP5A1 KLHDC3 ADORA2A /// SPECC1L SPRED1 C20orf30 BAXHERC4 ARAF 1557410_PM_at NRD1 MAT2A 241865_PM_at 237517_PM_at RASA2C20orf196 244249_PM_at MYEOV2 1559589_PM_a_at_EST21 ABTB1 243233_PM_at226347_PM_at MTPAP ZBTB3 NAP1L4 YTHDF3 ZFP36L2 COX5B ANKRD55 JMJD1CAGPATI SELM DAD1 ZNRD1 KIDINS220 DKC1 MCTP2 CAPNS2 ISOC2 PPP1R11242134_PM_at 229635_PM_at KLF7 SLC35B2 NDUFA3 1569362_PM_at MED27FAM159A 243013_PM_at 241838_PM_at TLR4 LOC100505501 CDV3 VAV2 ABHD14B213574_PM_s_at SRD5A1 CYP2W1 244349_PM_at_EST22 COX5B 229327_PM_s_atTBC1D14 244418_PM_at_EST23 SRGN ERICH1 244781_PM_x_at MLXIP SF3A2230154_PM_at NUDC PPID CLPP APOB48R C12orf45 MRPL43 ATP6V1C1 ACSL1 PTENND3 /// SH3KBP1 CARD16 /// CASP1 C5orf56 CAMTA1 TNRC6B CNPY3232784_PM_at 240798_PM_at KLHL28 HIRA CD44 TOMM22 SLC25A37 MBP SNPH237165_PM_at NELF SLC41A3 LPP MRPL46 FARS2 NDUFA2 ARPC5L CSRNP2 ZCCHC6GPN2 238183_PM_at JAK1 TRIM25 233094_PM_at ZNF83 ZNF2 TAF15 IL32 10-SepHLA-C LTB 241774_PM_at BMPR2 RPL10L 221995_PM_s_at_EST24 1558371_PM_a_atTMEM101 TUG1 IFNGR1 SMEK1 TRUB2 227121_PM_at 228826_PM_at_EST25 UBE4B216568_PM_x_at LOC100287911 C7orf16 LOC100287482 244860_PM_at KBTBD4 ///PTPMT1 NEDD9 PRPF18 GNB1 USP37 NDUFA7 1556942_PM_at H6PD CTNNA1 ZC3H11ACOX8A LARP7 LEPREL4 DNAJA4 NFAT5 1560102_PM_at DNMT3A ERICH1 CDKN1CPHF20L1 CD81 RALB 236962_PM_at CFLAR 1557987_PM_at MPZL1 CCL28 NIPAL3ZBTB20 EZR 1560026_PM_at TSEN15 FGD2 RBKS SURF2 MAEA DTX1 TMOD1 ///TSTD2 COG2 EPRS ST7 LOC729683 PYGM SBNO1 RBM42 PTK2B LOC401320 SNTB2242556_PM_at APBB1IP ZNF341 TNPO2 TOR3A UBXN4 235107_PM_at WDR77 FHL1SETD5 MDM4 HBA1 /// HBA2 MRPS22 243032_PM_at_EST26 ADK MAST4231205_PM_at 216380_PM_x_at_EST27 RBPJ RPP25 1557810_PM_at TRAPPC4FAM162A GKAP1 ZNF569 CKB WDR11 C19orf42 UBE2F CANX 240695_PM_at INVSLOC283485 1558624_PM_at_EST28 BIN2 CYP3A43 SCP2 SIVA1 242279_PM_atNHP2L1 CSH2 240652_PM_at_EST29 USP4 PCCA DVL2 HOXA1 C19orf20 SAMM50CD164 C11orf49 240220_PM_at ENTPD1 CFL1 SLC11A1 ZNF689 MED13L INHBBC9orf86 235999_PM_at HLA-E PAPOLA IVNS1ABP 235743_PM_at NT5C2 GSPT1 PDXP/// SH3BP1 238420_PM_at SNX5 TUBGCP5 CHTF8 NUBP2 FBXL3 SPPL3 FKBP5 MSH6PSMD4 PFDN6 FGFR1OP2 CFLAR PSMF1 NASP SECISBP2L ACAD9 LOC100127983HAVCR2 ARF1 LOC284454 DRAM1 CDKN2A B4GALT1 PPM1K CARD16 SLC40A1 PHF19243149_PM_at STAG3L4 PRKDC C16orf5 SKP1 GTDC1 RRN3P2 C11orf59 MED13RINT1 SLC8A1 PIP4K2A SNORD89 CSTB ILVBL GCLC 235288_PM_at 244025_PM_at241184_PM_x_at EMILIN2 PCK2 COL1A1 GPR44 NGLY1 TXN2 TTC1 FAM195A ANKHD1/// ANKHD1- KHSRP JAK2 DENND3 EIF4EBP3 DLEU2 ENTPD1 UTRN NOSIP PGLSC19orf60 TMEM126B 241466_PM_at RALGAPA2 TOX4 240538_PM_at NCRNA00182230733_PM_at_EST30 CCDC13 ZNF638 CAB39 1557804_PM_at_EST31 DGUOK FAM178A215029_PM_at PKN2 SLC22A15 ABTB1 PGBD4 GGNBP2 SDHC KIAA1704 /// FXNLOC100507773 SLC25A43 PIGW ELF1 HEMGN CHMP6 LOC100287911 G6PC31566473_PM_a_at CSTF2T 216589_PM_at GYPB FXYD2 PELI3 C1orf93 CFLARC9orf30 DNASE1L3 PLXNA2 SURF4 ZNF532 ARPC5L 232354_PM_at DUSP8 GPR97ITSN2 MPZL1 ECHDC1 ISCA2 PURB PKP4 POLH DHRS3 240870_PM_at APOBEC3GKRTAP1-3 FBXW9 ORAI1 239238_PM_at 237655_PM_at IKZF4 SYNCRIP ALPK1DYNLRB1 ARHGEF10L NUTF2 NUMB RPRD1A SHPK 238918_PM_at DNAJB12 C17orf104237071_PM_at CD58 UCK1 RAD50 237337_PM_at DLEU2 ASPH STX8 FCGR3A ///FCGR3B SPTLC2 FUS AARSD1 HIVEP3 B3GALT4 PRDM4 CRCP LOC728153239405_PM_at MBD3 FBXO31 SYTL3 TSPAN14 GGCX GRAP2 NUS1 /// NUS1P3 AVENUBA7 C18orf55 HBA1 /// HBA2 213879_PM_at NDUFA13 OS9 KPNB1 DPH1 ///OVCA2 CFLAR 1565918_PM_a_at RTN4IP1 NACC1 JAK1 SF3B5 IKZF1 MTMR14 SSR4215212_PM_at ZNF570 SLC9A8 SLC35B4 244341_PM_at ACIN1 MRPL52 LARP4B PECIC12orf52 BRD2 AP2B1 CORO1B 216143_PM_at ARF1 232744_PM_x_at BACE2LOC646470 RAD1 MGC16142 TMED8 ALAS2 234278_PM_at TFB1M TCOF1 CHMP4BTECPR1 C12orf39 TBC1D12 SEC24D 9-Sep STS SYTL3 PTGDS NUDT18 SUCLG1239046_PM_at DDX46 HBD PLEKHA9 230350_PM_at ARHGAP24 NARFL ATXN7 ZNF493CSF2RA ARMCX6 TMEM55B RPL36AL SMARCE1 PPFIA1 MAN2C1 C15orf41 GOLGA2P2Y/// GOLGA2P3Y RALGAPA2 MRPS17 /// ZNF713 ANKRD19 TPI1 MED13L237185_PM_at LOC144438 CD83 CELF2 DPH1 /// OVCA2 222330_PM_at RALGAPA1PELI1 235190_PM_at CHMP7 PGAM1 MLXIP KLHL8 CPAMD8 C7orf68 244638_PM_atABCG1 244454_PM_at 238544_PM_at HIPK1 MTMR3 LOC100130175 C1orf135 FNDC3BDCAF7 239850_PM_at TMOD1 CCR9 ARF4 HDAC3 DLD HIPK2 RUNX3 SLC15A4 PPP4R1LTLR4 242106_PM_at C12orf43 FAM105A HIPK1 C11orf73 LOC100288939244648_PM_at SMG7 EIF4E2 AP1S1 FAM45A LLGL1 DDX6 239379_PM_at1562468_PM_at APH1A RABL3 FOXP2 PARP2 241458_PM_at ILKAP GRHPR ESRRADICER1 RBM47 HAS3 CNTNAP3 C17orf49 MDP1 ZDHHC19 STYXL1 VNN3 NDUFAF3 ZFP3ANKRD54 CDKN1C TRAF3 B3GNT6 230395_PM_at ZNF593 IRF8 ALG1 LOC100288618SMARCA2 217572_PM_at CYP4V2 GANAB 241388_PM_at TMEM93 UBR5 228390_PM_atIL1R2 EZR GFM1 C22orf29 BRD2 GDF15 1565597_PM_at DCAF6 /// ND4 TPST1243158_PM_at BAGE2 /// MLL3 BRCC3 GAR1 KIAA0141 OGT SRSF3 PTPN1 FHL3PLIN4 227762_PM_at RNF126 SRSF9 231695_PM_at KIAA1109 COQ6 BCL6 CBR3ROPN1L SDHAF1 232081_PM_at CALR C17orf44 ERLIN1 ARL6IP4 1561893_PM_atLAGE3 SMAD4 LOC151438 NFKBIE ALG2 ARHGAP26 VPS39 TMEM189 1569854_PM_at241091_PM_at C17orf90 SSX2IP LOC729683 YY1AP1 CD7 SRP72 231652_PM_at1562265_PM_at TLR8 NDUFB10 STK24 MYLIP SLC2A4RG CDC42EP3 IPO4 HINT2 LFNGMYST3 C9orf16 PHF20L1 SSBP4 MED7 LOC100507602 MCTP2 ECHDC1 1570639_PM_atRNF115 1554948_PM_at 243837_PM_x_at TMEM189-UBE2V1 /// 1556657_PM_atUBE2V1 TBC1D5 NUDT22 HNRNPH2 MLC1 CR1 CUEDC2 KLRC3 TBC1D25 EDF1 UBIAD1PHKA2 FBLIM1 GSTA4 FAM134C 240803_PM_at 217152_PM_at ZXDC DCUN1D1 CFLARALOX5 MPI E2F2 USP48 VAMP3 PKP4 SIPA1L2 WDR8 DEFB122 PATZ1 ZNF561 H1FX231934_PM_at CCL5 FAM113B KLHL36 214731_PM_at GTF2H2 CDC42 GRPEL1 SUPT7LPMM1 TNPO3 APITD1 TOX AHNAK DUSP7 NEK9 PTEN SH3BGRL 1569528_PM_at ZNF524SAMM50 1560926_PM_at RPL18A /// RPL18AP3 LPP 241508_PM_at ASH1L NDUFC2MARCKSL1 TPI1 RBM3 SHISA6 NSFL1C ZNF580 NPM3 ADAM10 GLYR1 1569727_PM_at1561872_PM_at ATL1 ATP11B 235959_PM_at DGCR6L NSFL1C ZNF37BP ASPHTSC22D3 NVL TBL1Y RPRD2 POLR2C COX2 YWHAZ USP28 MIPEP 1560868_PM_s_atPRPF19 239571_PM_at FKBP5 C1orf128 1565894_PM_at LAT /// SPNS1 POLR3KMED19 RPS19 EIF4E3 TSC22D3 MXD4 1564107_PM_at CDS2 CALM3 TMEM179B RASSF4CXorf40B NUFIP2 243659_PM_at ECHDC3 TMEM161B NCR3 PREPL N4BP2L1 MRPL42UBE4B PSEN1 OLAH C22orf32 KCNK17 HIRIP3 HDAC4 PIK3CA TMEM69 PRO2852KCNJ15 HEY1 1557852_PM_at MRPS24 PRKAR2A TTBK2 RNASEH2C NAF1 CYSLTR1NAMPT AGPAT1 241219_PM_at 1559020_PM_a_at AHDC1 1565614_PM_at241993_PM_x_at TMEM48 LYST MED27 TOM1L2 PHC1 1559119_PM_at PRPF6 LYSTCABIN1 242611_PM_at NDUFB6 242407_PM_at OCIAD1 PATL1 SORL1 LIMK2 CCR2FAM82A2 PATL1 IL2RB WDR48 HSPA4 242480_PM_at ADI1 UBE2G1 RHOF221205_PM_at C12orf76 /// MLL4 240240_PM_at LOC100510175 UBE2D3 PNPLA4ETV6 GABRR2 MBOAT2 HIST1H1T CENPB AFG3L2 ZNF576 GOSR2 EP400 RGS14 U2AF1VSIG4 1560625_PM_s_at SKP2 U2AF1 RPL23AP32 KDM5A SH3BGRL TKTL1 APCCIAPIN1 BEX4 240960_PM_at MRPL12 CEP170 /// CEP170P1 PNPO 243546_PM_atSGSM3 CASP2 239759_PM_at IL6ST LRP10 ZNF696 TMEM229B PON2 MMP8 CFLAR1556043_PM_a_at 213945_PM_s_at ICMT PLSCR3 241441_PM_at LOC339290PACSIN2 PDE7B FPGS CCL5 DSTNP2 C5orf4 NR1I3 C11orf83 NFATC2IP ELAC1 DR1243072_PM_at ATP5D SAMSN1 POLA2 1559154_PM_at FASTK ZNF23 ENSA215109_PM_at ZBTB11 SUMO3 RGS10 C5orf41 PPP1R3B MRPS12 PDE6D EGLN2239124_PM_at ZDHHC24 PSMB5 1568852_PM_x_at PLD4 GLTPD1 NDNL2 UGP2 NSUN4RBMX2 CHD6 TMEM141 ARPC5L ACRBP RUVBL2 KLRAQ1 242926_PM_at NHP2 MAP3K7MGEA5 PEX7 GNAQ 242142_PM_at C14orf118 226146_PM_at C10orf46240780_PM_at KLHL8 HIC1 CRKL LOC729082 RERE ANKRD11 C6orf26 /// MSH5241786_PM_at CIAO1 TNFRSF21 FAM104A ROBLD3 SEC61B ZMYM6 TWISTNB GSTO1INPP5D 239901_PM_at FAM50B PDZD7 SRI IL12RB1 TMEM201 EIF3F PATZ1 KPNB1SNX13 PSEN1 241590_PM_at NCOA2 PTPN9 DUSP14 ADAM17 238988_PM_at MMACHCGPBP1L1 LAT2 THOC4 SAV1 CASP4 MUDENG F2R CUL4B PTENP1 ANAPC11 FGD4 RBBP6CLCN7 ASB8 ABCF2 232478_PM_at DOCK11 TPCN2 DDX28 SFXN3 PECAM1 SNX27ATG2B GATAD2A FLOT1 238733_PM_at PTPMT1 MLYCD 243787_PM_at ALDH9A1 PSME3BAT4 TPM4 TBL1XR1 RASSF4 PLA2G12A OLAH FAM126B ZNF598 C7orf28B /// CCZ1233270_PM_x_at ATF6B NEDD9 TK2 CHAF1A CHCHD10 230123_PM_at UHMK1 MRFAP1MYLIP ARIH2 NCRNA00094 GATM NENF NDUFB9 WNK2 TXNIP UQCR10 SMAD4 TAOK3IL28RA TGIF2 PIK3AP1 ALMS1 AVIL BCL7B MRPL14 HBA1 /// HBA2 ACTG1 EP400GFER CASP2 LOC645166 RFX3 TELO2 PFKFB2 MRPS10 NCR3 C9orf23 ENTPD1 ANKZF1CHCHD8 RFTN1 SRSF5 PDGFA RAB5C 215392_PM_at SMARCA4 217521_PM_at244772_PM_at GUCY1A3 LOC401320 PHAX 222303_PM_at CCDC123 238619_PM_atTTLL3 SART3 PFDN1 PHTF2 PPIL4 /// ZC3H12D WWC3 235716_PM_at MKLN1 URM1240231_PM_at FAM173A DHX40 TSFM AKAP17A PTGDS GADD45GIP1 ST7L ZFP91229249_PM_at 233808_PM_at 235917_PM_at 239809_PM_at TET3 KIF3B SAAL1235841_PM_at PON2 1562669_PM_at PDE6D ATP5G3 242362_PM_at STX7 HNRNPDCCDC107 COX5A LYRM4 ASCL2 GPX1 C20orf30 NUDT4 /// NUDT4P1 KREMEN1 RHOCC19orf53 P2RY10 CD163 VPS13B GTF3C5 TES TMEM43 TNPO1 DEF8 PRDX1 TRGV5IL1R2 SPTLC2 ITCH C11orf31 DNMT3A KLF12 CX3CR1 243229_PM_at C17orf90TROVE2 MOBKL2A KIAA0748 C19orf52 RABGAP1L NUCKS1 C8orf33 CYTH2 EMB ///EMBP1 C8orf42 FBXO9 MYLIP CCNK FBXL12 CMTM6 CORO1B 237330_PM_at PID1KLRB1 JMJD4 UBTF TNFSF8 ANXA4 UGGT1 239474_PM_at NOL7 TMEM64 YIF1A ELF2SLC29A3 HNRNPUL2 233010_PM_at LOC550643 PIGP 241145_PM_at CHD7 PDE4AC5orf41 238672_PM_at MBNL1 CUTA HLA-E PHF21A TXNIP NSFL1C MDM2232205_PM_at DPP3 1569238_PM_a_at IRAK3 MDM4 GLE1 DNAJC30 ELK1 TFAP2ENCRNA00094 PLA2G12A IDH3A 243236_PM_at 232307_PM_at RPS15A GMPPB DUSP5ELOVL5 CTSC TFCP2 CD36 CNO 229548_PM_at 1557238_PM_s_at DGCR14 CDV3ABHD6 ZDHHC19 INF2 MDH2 RBMS1 232726_PM_at ITGB2 RHOF PSMB6 244356_PM_atRIOK3 STK17B SCFD2 HECW2 RALBP1 240271_PM_at SMARCC2 1557633_PM_at TBRG1KLF9 CISH 243578_PM_at CYHR1 237881_PM_at ADAM10 DLEU2 BMP1 ANKHD1GPBP1L1 LOC285830 PLBD1 DENND1B UBE2D2 RPAIN NOL3 NSFL1C HIATL2 SP1SFRS18 RAB6A 232963_PM_at SLC2A4RG BID N4BP2L2-IT 233867_PM_at242865_PM_at MRI1 240867_PM_at PRR14 DDX51 STS COMMD5 1568903_PM_at242232_PM_at 234578_PM_at PSMD6 PPP1R7 233264_PM_at SNX3 AKAP10 ARL2BPECE1 ARNTL NCKAP1L IKBKB SF3A3 UTP20 IQGAP1 CYC1 NUP50 GPCPD1 BTF3ZNF644 RSRC1 PRKAB2 C5orf22 NUDT4 CR1 1556518_PM_at MAPRE2 AES ALG14233406_PM_at 236772_PM_s_at ZC3H7B TRBC1 PITRM1 240326_PM_at LHFPL5MAP3K2 TMEM64 LOC100128439 REPS1 RNF24 TRIOBP 7-Mar EWSR1 /// FLI1238902_PM_at LFNG RSBN1L LOC100271836 /// AGAP2 PAM LOC641298 ARRB1DDX28 BAZ1A MON1B PAOX N4BP2L2 236901_PM_at NDUFB8 /// SEC31B ZDHHC17RASSF7 TCTN3 HIST1H4C ZNF394 242461_PM_at ACRBP STX11 C22orf32 YWHABWDR61 235466_PM_s_at PELI1 KIAA1267 UBA7 MRPL9 236558_PM_at PABPC1 ///RLIM SH2D2A 1562982_PM_at TMED9 233315_PM_at CSF2RA ATF2 238320_PM_atSMAP1 PRPF31 TIMM17A NLRP1 LOC100190939 NF1 ZNF641 GPR137B MED13229668_PM_at DNAJC4 NDUFA8 LOC100127983 CHCHD1 ASXL2 POLR2C PREX1 TAOK2FBXO16 /// ZNF395 FAM118B PAIP2 MGMT DPY19L4 ASAH1 232952_PM_at1566201_PM_at NBR1 MRPL10 STS ATPBD4 237588_PM_at TTYH2 UBE2D3 MFNGNUDT16 HECA MRPS25 HIPK3 FAM65B COX3 LOC100133321 SPAG9 XIRP1 REST PUSL1243888_PM_at BECN1 242126_PM_at ZDHHC24 C10orf58 RNF126 ARRB1234326_PM_at RSRC2 ZNF207 RBBP9 WWC3 FAM86B2 /// FAM86C /// DNAH1LOC645332 /// LOC729375 STX16 216756_PM_at CD160 C10orf78 232095_PM_at229670_PM_at AK2 YY1 C19orf33 RAB30 LOC100128590 223860_PM_at CISD3CNOT2 PABPC3 CRADD PFAS MAPK7 UPF1 229264_PM_at FLJ31306 WDR48 LAMA31568449_PM_at TBC1D7 241692_PM_at RASSF5 LARP1B MDM2 PSMC2 1570165_PM_atLSG1 GSTZ1 243178_PM_at SAP30L 1563092_PM_at 222378_PM_at C6orf129 FDX1ADO KLHDC10 1562289_PM_at UBE2J1 NFATC1 PPFIA1 CALM1 PRSS33 PHPT1C1orf122 MYO1C MBD3 POP4 244026_PM_at HSBP1 COX4NB 244536_PM_at RANBP9EFEMP2 DYRK1B STOML1 241688_PM_at UBE2E2 TUFM SSH2 NDUFB10233810_PM_x_at PDLIM2 ZDHHC16 LIMS1 PDIA3 PSMD9 OAT C17orf106241595_PM_at BCL7A RAB34 242403_PM_at 229841_PM_at PSMA5 SEC61G GSK3BRWDD2B RAD52 LOC221442 KBTBD2 NDST2 VAV3 GNL2 ARF5 TFAM TAAR8 PACS1 LCOR235592_PM_at 227501_PM_at CACNB1 238954_PM_at C1orf50 ADAM22 MAP4K41565975_PM_at MIA3 MCL1 LRRC14 WIBG RPS4X /// RPS4XP6 BCL2L11 C11orf31OPRL1 ZBTB7A TRPM6 BCKDHB C11orf31 237396_PM_at 215147_PM_at LOC728903/// MGC21881 241658_PM_at USP8 NANP NUMB ZC3HAV1 E2F3 ONECUT2 PPP1R15ASLC9A3R1 CYTH3 RHOH MALAT1 YLPM1 EXOC6 PIK3R2 INO80D LRRFIP1 MDM2 R3HDM1SNX24 PRDX6 239775_PM_at TMEM107 RBMX2 FOXO3 /// FOXO3B C15orf63 ///SERF2 ZNF395 244022_PM_at 230970_PM_at LCP2 ITM2A TOMM22 ACAA2 CRY2 PHYHFLJ12334 FXR2 GGA2 XPA 232113_PM_at ILF3 CCL5 DDR2 VPS13B ANKLE2 HOPXSEC11A CSHL1 1560271_PM_at CPEB4 DVL1 MLL3 GNS AGER PREB PSMA1 SRSF9C1orf151 SUB1 MED21 AP1S1 FRYL TSEN15 C20orf11 SPCS1 CMTM5 ECHDC1 CRYZL1PADI4 C7orf30 227384_PM_s_at ORC5 MEF2A HIST1H4J KCNE3 SLC16A3 IL21RLOC100129195 ACTG1 MRAS EDEM3 NAT6 SNORA28 N4BP2L2 C7orf30 216683_PM_atWTAP TSN CHMP4A SERPINB5 MED31 NUDC MRPL52 TRD@ COMTD1 OCEL1 SRSF1 RBM43JMJD4 LOC441461 ARFGEF1 FLOT2 SFRP1 223409_PM_at MAN1A1 BOLA3 PPP3R1 ///WDR92 1557796_PM_at VAPA 237442_PM_at QDPR LOC284009 TGFBR2 HLA-E RPL23MLANA 232472_PM_at LOC648987 HMOX1 PTP4A3 GNB5 RICTOR NINJ1 CX3CR1 MYLIPARID4B C9orf119 TMEM53 GTF2A2 ANKRD57 PSMF1 PWP1 ZNF688 DNAJB6 ///TMEM135 RNF17 MRPL18 ATP6 PPTC7 CATSPERG PTAR1 RNF14 MRPL16 222315_PM_atDPY30 /// MEMO1 NCRNA00116 LSM2 KCNE3 SPATA2L PIK3R5 COX5B FNIP1 FAM82BARFGEF1 COX5B ZNF80 BTF3 C1orf63 HSD17B10 ANKS1A ZNF638 ATP2A3 COX19ATP5SL 216782_PM_at EIF4A2 CRK 239574_PM_at SUGT1 ND2 NOP161555977_PM_at MBP PITPNC1 ZBTB40 RNF5 LOC100507192 SFSWAP HIGD2A ///236679_PM_x_at KCTD20 LOC100506614 FKBP15 CRIPAK BLVRB SCAMP1 ZNF238C1orf128 232406_PM_at B3GALT6 MAZ QRSL1 LOC100134822 /// TRAPPC4LOC100288069 NUPL1 TMEM107 RTN4IP1 SBF2 220691_PM_at MTMR11 HIBADH FANCFCDKN1C GLUL C14orf167 GRPEL1 C9orf69 COPB1 CYP19A1 ATP8B1 SMAP2 RASA4/// RASA4B /// 239570_PM_at ZNF24 RASA4P ACSL1 FBXL21 VAPB SPECC1 MBD4RALGDS SDHAF2 CHD1L FCER1A PTPRCAP 1556462_PM_a_at POP5 241413_PM_atNSUN4 242995_PM_at 1564154_PM_at 240094_PM_at UROD ACVR1B LOC728392 ///NLRP1 DPM3 1560230_PM_at C12orf57 SSNA1 236572_PM_at FCAR 231111_PM_atFRG1 ATP11A 1564733_PM_at C1orf58 DCAF7 SASH3 NRD1 TPM4 237586_PM_atMRPS18B FAM110A CPEB3 240123_PM_at 231258_PM_at NUDT16 241936_PM_x_atNUP37 PIAS2 MTMR10 QDPR AIF1 LGALS3 XRCC5 CPD PRKAR1A 2-Sep DAB2 RSRC1238064_PM_at 239557_PM_at GTF3C6 CHCHD2 TEX264 IVD DHX30 HBB TMEM41A236495_PM_at SENP5 LPAR1 RAB4A RBKS SAMHD1 SHMT2 TAF9B DPP3 SSR2 NID1RBMS1 ABHD6 1556007_PM_s_at 239331_PM_at 239721_PM_at SLC12A9 DYNLRB1241106_PM_at MBD4 1565889_PM_at CCDC93 WDR61 RAB6A OTUB1 C1orf77 ACACBKIAA0319L 243663_PM_at ZNF70 MRP63 MKKS FAM96B TNFAIP8L1 TNFRSF10A PMVKLOC100130175 EML2 SERBP1 STK36 LOC401320 MCTP2 CYB5R3 VNN2 CHD4227505_PM_at ZNF384 RBBP4 242612_PM_at TSPAN32 1563075_PM_s_at FAM192ARPS26 214964_PM_at EVL IKBKE PDCD2 236139_PM_at LPIN2 216813_PM_atNCRNA00275 QKI ZNRD1 MRPL24 SLC2A8 WBP11 FLJ38717 ZNF75D RLIM EXOSC6DND1 1566680_PM_at MRPL20 DNAJC10 ACLY 242306_PM_at STK38 PLCL2 CSN1S2APCSGALNACT1 242542_PM_at BOLA2 /// BOLA2B 236528_PM_at 242457_PM_atPIK3C3 236338_PM_at ARHGAP26 SNW1 1560342_PM_at MAPK1 SPG20 PPARA TRA2A243423_PM_at C10orf93 RUFY3 UBE2B 240137_PM_at 236005_PM_at SEC61A1UBE2D3 244433_PM_at COQ4 SUMO2 SRSF4 FBXO9 GTPBP8 242857_PM_at PPP6R3IL13RA1 LOC148413 NCOA2 TIGIT FBXO38 1565913_PM_at CANX ASB2 IMMP2L RIN3ZNF561 RHOT1 242125_PM_at C15orf28 ISYNA1 LAMP1 SEMA4D 239859_PM_x_atVHL ATP2A3 RTN4 242958_PM_x_at KRI1 FAM13AOS GDI2 FLJ44342 PDCD6IP ZBTB4ATXN7L1 240547_PM_at DNAJC17 SELPLG STK35 C9orf16 GDAP1L1 233369_PM_atSRGAP2 GAS7 EIF4EBP1 FLJ39582 1557637_PM_at ZNF148 215369_PM_at233876_PM_at FAM174B TMEM5 GPRIN3 NSMAF 238888_PM_at FAM126B C17orf63PPP1R2 ALG13 CSNK2B /// LY6G5B STAU1 BTF3 USF2 ASAP2 ZNF4391560706_PM_at ADAMTS2 ZNF407 CSTB 236883_PM_at CABIN1 GLI4 RBM47 FAM98BTCP11L2 AKAP13 240497_PM_at HP1BP3 RBM25 ZBTB16 TADA2B CELF2233473_PM_x_at 1557993_PM_at MRPS6 COPG2 HIST1H1T TTF1 HEATR6 ZNF148SNCA NUDT10 MLL3 DNAJC3 229879_PM_at COQ4 MDH1 C17orf59 SLC24A6 GLT25D1SELENBP1 POLR2I PXK IKZF2 SLC7A6OS UBE2H TAGAP GCNT7 MTMR11 LPAR2 ATP5G1GPATCH2 NFATC2 TNPO1 AKT2 NMT2 244373_PM_at CCDC97 SLC8A1 ADHFE1 NOLC1KCTD5 DIP2B ZNF784 C3orf21 238563_PM_at MKRN1 CYBASC3 SIPA1L2 RNF4 MALBAT2 HDDC3 HBB ZKSCAN1 APP SBK1 TMCO3 ATF7IP 241630_PM_at PFDN2239245_PM_at C1orf174 MRPL45 DPY19L1 CDK5RAP1 TAGAP TMEM161A MYL6 FAM43ALSS 240008_PM_at ZNF703 ATP5S 1565701_PM_at AGTRAP RBM47 LRCH41560332_PM_at 234164_PM_at CD226 ANKFY1 CAPZA2 241152_PM_at ORM1 STK32CCOX6A1 HBA1 /// HBA2 1560246_PM_at TMCO3 SNORA37 TMX4 242233_PM_at IL21RCSTF1 TNFSF4 ZNF398 LOC100506902 /// EXPH5 243395_PM_at PIKFYVE ZNF717NDUFB11 CD74 NOL7 LRP6 FAM193B NPY2R 236683_PM_at C9orf86 BCL6 ATP13A3TNRC6B SHISA5 C6orf106 GIGYF2 UFM1 MCTP2 C14orf138 MAP4K4 FHLOC100128071 FAM65B THAP7 ESYT1 PRSS23 1569237_PM_at ACP1 LGALS1C1orf212 231351_PM_at SLC22A17 238652_PM_at NDN GATAD2B DLEU2 /// DLEU2LDIMT1L TBC1D22A NDUFS6 BBX TRIM66 242471_PM_at GRAP ESD MTPN LOC146880KLF6 FAM49B SNRNP27 MYOC SLC6A6 CARS FAIM3 SMOX GHITM PLEC TTC39C MBD2FLT3 ENTPD1 RBL2 JMJD6 1566257_PM_at LOC285370 CHMP1A ACTR2 FKBP2 PRDX6UQCRB 1562383_PM_at HPS1 CBX6 ERCC8 GPM6A 229673_PM_at C1orf21 CCNB1IP1LOC284751 SGCB ORMDL3 CC2D1B 239045_PM_at HGD RHBDD2 241444_PM_at TFCP2229571_PM_at LSG1 NRG1 237377_PM_at KIF5B DNAJC30 JAGN1 COPZ1 CSADENTPD4 RAF1 ATP8B4 MBP PCMT1 FAM181B AMZ2P1 HBB COX2 1561915_PM_at PARLCTSZ TIMM13 SH3GLB1 PROSC AFF4 CAPRIN1 TMF1 C1orf85 RBM6 TASP1 SSRP1CISD1 PGLS 1558588_PM_at IGF2R COPA FKBP5 ZCRB1 EPRS ZBTB4244048_PM_x_at SNAP23 IGLON5 1558299_PM_at 2-Sep FLJ33630 UQCRB ZNF333P4HB ACPP HUS1 SPOPL PFDN6 CNST STX16 DUSP28 ARL16 SLC8A1 LOC100287227PDZD8 ABHD5 CDK3 237710_PM_at GPR27 SCFD2 A1BG RBM22 TMEM30A ITPA243217_PM_at LOC100286909 RPL23A 236889_PM_at 1557688_PM_at UBIAD1C20orf196 236168_PM_at SLC8A1 SNTB2 238243_PM_at TMED3 ARHGEF40 CTBP1NOP16 RARS2 TMEM134 EIF2B1 SNAP23 PALLD PLA2G7 C12orf75 ZNF542 RALGAPA2216614_PM_at 243222_PM_at SSR1 RP2 SERINC3 CASC4 5-Mar FAM162A PRPF18CTSO C1orf151 PTGER4 RNF144B ETFB DDX10 ASPH ZCCHC6 SOCS2 RBM3 SPTLC11564568_PM_at 220711_PM_at NUDT3 MAP3K3 SAFB TTC12 234227_PM_at234151_PM_at CD163 TRAPPC2 /// UBL5 TRAPPC2P1 PSMB10 CCNL2 ALS2 GUSBP1INSIG1 DLEU2 COX17 1559452_PM_a_at ASPH FLJ14107 ABHD15 MSH61565598_PM_at EML2 ZFYVE27 BAGE2 /// BAGE4 PEBP1 244474_PM_at TNRC6BMIDN CXorf40A NOP16 ADPRH LONP2 239383_PM_at CLPTM1 ANKRD36 PPM1LAKIRIN2 LOC285949 IDH2 ZNF608 240146_PM_at 1563076_PM_x_at TP53TG1 HAX1BMP2K CXorf40A /// CXorf40B FCGR2C GIMAP1 240417_PM_at THAP11 FAM13AISCA1 SH2D1B 1565888_PM_at 1565852_PM_at 215204_PM_at FBXO25 BNC2 JMJD8C1orf25 GRB10 RNF214 TRIM46 RNASEH2B 1558877_PM_at 235493_PM_at PDE8ATCP1 KIAA1609 MUSTN1 MTMR3 RAP1GDS1 C2orf18 SRPK2 DNAJC8 GP5 CMTM8COMMD7 CCNL1 228734_PM_at RNPEPL1 CLCN3 TMEM204 UBB TMEM160 C11orf48WDR1 SMARCB1 GRB10 RBM15B GPER 236592_PM_at LOC100129907 227479_PM_atC11orf31 NCRNA00202 DGCR6 /// DGCR6L BMPR1B TMEM191A C5orf30 MRPS18ACD163 APOOL ARF1 PCGF3 CHN2 242235_PM_x_at SLC41A3 DCTPP1 243338_PM_atSBK1 FGFBP2 244732_PM_at EPS15L1 240839_PM_at ITCH 2-Sep CHCHD5231513_PM_at PACSIN1 SERBP1 ZNF689 C1orf144 DUSP1 C19orf56 ARL6IP4C22orf30 HAVCR1 ING4 ETFA HMGN3 242527_PM_at LSMD1 WDR61 TEX10 ATF6BADPRH MRO C1orf27 TRAF3IP3 C7orf68 ZNF169 FKBP1A TGFBR3 CACNA2D4 MYST3ATP6V1C1 ICAM4 EXOSC7 GATAD1 DLEU2 242075_PM_at CCND3 RAB9A1564077_PM_at C5orf4 TLR2 FAM120C PRNP AMBRA1 TRABD C7orf53 RPL15 AHCYAKR7A2 RGS10 HCFC1R1 RNF14 231644_PM_at RYBP SERPINE2 UEVLD ACP5 CD247NFKBIA RGS10 239845_PM_at TRADD 232580_PM_x_at ENO1 ARHGAP26239274_PM_at ME2 SLC16A6 MCAT CBX5 TNF GTPBP6 /// LOC100508214 ///LOC100510565 NUCKS1 STAT6 H2AFY APOOL LRCH4 /// SAP25 HEATR2 PSME1 KLF4LOC100131015 GUSBP3 WHAMML2 MS4A7 CFLAR 243992_PM_at FLJ38109 DRAP1MIF4GD UCKL1 TAP2 244607_PM_at RBCK1 DYRK4 C6orf89 232615_PM_at PTPRONGLRAP1 PSMA3 236961_PM_at 227082_PM_at PPP2R5E 1557456_PM_a_at1563487_PM_at 241974_PM_at SNCA 1559133_PM_at EGLN3 TMEM1021555261_PM_at IFNGR1 239793_PM_at IL13RA1 PPP1R16B ZNF397 RBBP7LOC100506295 SPATA7 FAHD2A PLDN LOC100510649 HEXB MPZL1 PSMG4 MRPS34240145_PM_at MOBKL2C WIPF2 RPP21 /// TRIM39 /// ABCB7 DPY19L1 ALG3TRIM39R KIL22 NAT8B SRD5A1 ZSWIM1 241114_PM_s_at AKAP10 SEMA4C SSX2IPSTAT6 243625_PM_at DCTN6 BSG RNF113A CRYL1 E2F2 242968_PM_at RPP38CDADC1 CDC42SE1 DNAJB4 1559362_PM_at PBX2 240019_PM_at PWP2 INO80B ///WBP1 SERINC3 244580_PM_at 243350_PM_at NLRP1 TP53 227333_PM_at SCP2239804_PM_at 240984_PM_at MEAF6 TOX2 CDKN1C 1556769_PM_a_at FCF1 ///LOC100507758 /// QKI MAPK1IP1L C19orf70 ADCK2 PNN AIF1L INSIG1 IMPACTZNL395 242901_PM_at COG1 GLTP 243107_PM_at 240990_PM_at EIF5 CMTM4242117_PM_at RPL14 XCL1 /// XCL2 1563303_PM_at GNL1 GTF3C1 ARHGAP27LOC100509088 C2orf88 NCKAP1 SNHG7 TMEM189-UBE2V1 /// HIST1H4J ///HIST1H4K PPAPDC1B UBE2V1 LIMK1 HMGB3 TDRD3 ATXN10 BOLA1 ZFAND2B TRIP12SORT1 235028_PM_at PTPRS ANXA4 NCAPH2 SH3BP1 CLCN5 SNHG11 TANK PSMB4CHCHD3 C17orf58 SNRPA1 PHTF1 UQCRFS1 PTEN OSBPL3 REM2 MED8 HRASLS5 BPGMEXOC6 RPP30 KRT10 TNS1 CELF2 1558048_PM_x_at SLC46A2 MRAS FBXO38 CXXC51562314_PM_at IVD TIMM23 /// TIMM23B SETBP1 SAC3D1 PRTG DNAJA4 DNAJC10TRD@ KLHL6 240638_PM_at BZW1 SRP72 TTLL3 HIGD2A ZDHHC2 215252_PM_atRHOBTB2 228151_PM_at 237733_PM_at ARHGDIA MARK3 FBXO33 MBOAT2224254_PM_x_at C19orf60 1560386_PM_at ETV3 215981_PM_at CTSB TMEM59FASLG SLC43A3 PRDM4 MADD SEPT7P2 1570281_PM_at 1563210_PM_at MDH2 CHAF1ADCAF7 HNRNPUL2 1566959_PM_at GSTK1 237341_PM_at ARHGEF2 NBR1 RAB27BATP5O DCPS PHB CXXC5 TSPYL1 DNAJA4 RC3H2 HCFC1R1 DIS3L ADCK2 GTF2H2BZCCHC6 239780_PM_at RPA1 PIGU KCTD6 PPP1R15A 216342_PM_x_at C16orf58CSRP1 NAALADL1 DGKZ IL21R 234218_PM_at ITGAM 244688_PM_at MRPL55 JTBCYTH4 C1orf21 NPFF FARS2 POLG FASTKD3 6-Sep 237018_PM_at DCAF12241762_PM_at 237118_PM_at PRKAR1A RNF34 TWIST2 CXCL5 HLA-DPA1 C14orf128TPI1 SLC25A26 HBA1 /// HBA2 A2LD1 239842_PM_x_at 1555303_PM_at1556107_PM_at LOC100505935 FCHSD1 234604_PM_at SYF2 JMJD1C 243064_PM_atSLC2A6 238552_PM_at SH3GLB1 239358_PM_at TRA2A NFIC NDUFB2 LANCL2 SSSCA1215846_PM_at DNAJC8 MDM2 IFNGR1 LEPROTL1 ZNL792 CD3D HBG1 /// HBG2 COX1DCAKD TMPO FAM162A ZDHHC4 RPL23A SDHC SORL1 UXS1 ALPK1 SCD5 HERC4 NDUFB5MLAP3L SLC4A11 LPAR5 1558802_PM_at RBM14 YWHAB POP4 CCDC147 ZNL330 RPL28C14orf179 6-Sep 238883_PM_at DCXR 232527_PM_at CLC RAB6A /// RAB6C 6-MarPHF3 227571_PM_at RAB8B MAP3K7 COX5A CFLAR RQCD1 KPNA6 243858_PM_atSDF2L1 ABR RFC3 CLEC10A CD3E PLEK2 SNRPD3 234807_PM_x_at UBN2 UFC1242997_PM_at CTBP1 MRPS9 TMX1 PPIE 9-Sep 216871_PM_at 241913_PM_atHLA-DPA1 GZMB MPZL1 233931_PM_at GLT8D1 VPS24 MTMR4 232700_PM_at GPR56C1orf43 NUP98 ZNL638 C12orf29 IRS2 TRRAP ZBTB20 HLA-G PEX26 USE1 GUCY1A3ITPR2 TP53RK FKBP1A VAMP3 METTL6 ZZEF1 238842_PM_at MBP MCL1 XPNPEP1MAN2A2 WWOX 229569_PM_at ATP6AP1 PTMA AFFX-M27830_M_at GTF2H5 TMED9CDK10 1557878_PM_at DNAJC3 PHF19 SAMSN1 NUB1 C3orf78 EIF5 SNX5 CBX3HLA-A /// HLA-F /// HLA-J TRAPPC3 AKAP8 233219_PM_at ILF2 244633_PM_atSERGEF SLBP TMED2 RIPK1 IGFL2 COX15 EPS8L2 243904_PM_at NOB1 PSPC1PLA2G6 1566166_PM_at NME7 LRRC8C ERLIN1 C2orf28 CELF1 1566001_PM_at JTBCD84 MRPL9 PXK CCND2 MTA2 CD3G 1558401_PM_at C11orf73 NEAT1 DHX35 CHRAC1SSBP1 TSTD1 PDS5B MGC16275 LRRFIP1 236752_PM_at FBXL17 1557224_PM_at241597_PM_at MRPL49 SLC39A4 /// SLC39A7 242059_PM_at LUC7L PLAGL2223964_PM_x_at MTIF2 MRPL34 VIPAR NDUFB7 PDHB GUF1 GSTK1 LIMD1 ACSL3217379_PM_at 241191_PM_at THUMPD3 ICAM2 PRPSAP2 THRAP3 GNLY EBLN2 COPS6PTGDS C12orf66 LSS YSK4 SLC38A7 HSD17B1 1563629_PM_a_at BCL2L1 RHOBTB3JTB TARSL2 CIRBP LOC100507255 240217_PM_s_at MBD6 1561644_PM_x_at SRRM2PDLIM4 QARS SIPA1L3 TANK 237239_PM_at 240665_PM_at 1556492_PM_a_atZNF511 FASTK LOC400099 239023_PM_at REPS2 SRD5A1 OPA1 TMEM147 ND61558695_PM_at IFT81 DKFZp667F0711 237456_PM_at RICTOR GATAD2A ZNPHT2PI4KB IFI27L2 OLAH CMTM3 KLF6 1559156_PM_at RAI1 ELOVL5 SNX3 RASA2MAGED2 GPSM3 FXC1 CYTH1 C12orf5 MOGS 244010_PM_at RBP4 ARHGAP24 MGC163841562505_PM_at 215397_PM_x_at RAB4A /// SPHAR 1558783_PM_at 237544_PM_at232047_PM_at ICT1 CDK11A /// CDK11B C7orf26 1563277_PM_at MED29 C14orf64AP1S1 MTCH2 SLC25A38 CPT1A SRPK1 TP53TG1 SPTLC2 COPS8 RNF216 HEXIM2CCNDBP1 EPB41L5 CFLAR SSH2 USP48 KPNA2 HBG1 /// HBG2 C12orf62242380_PM_at APLP2 233239_PM_at KRT81 MRPL54 ARHGEF40 RAP2B BAT4225906_PM_at PABPC1 1563958_PM_at C19orf40 HELQ RALGPS1 BCL6 GNLY NOTCH4DNAJB14 NAA10 CEP152 HBA1 /// HBA2 PSMA1 LSM12 242875_PM_at IL1RAP COTL1240636_PM_at 239716_PM_at SLC6A6 RPS19 NSMCE1 1559037_PM_a_at CPT1ATRIM41 HADH PHF21A USP25 243469_PM_at PPTC7 PSMC5 216890_PM_at INSR242167_PM_at IL13RA1 236417_PM_at TCEB2 PHB2 GPA33 239923_PM_at NCOA2LPP TMEM203 PODN ETFDH PEMT BTBD6 N6AMT2 GAS7 MRPS12 PDE1C 242374_PM_at227897_PM_at NECAP2 UBN2 NDUFS3 222626_PM_at GRB10 BAK1 FAM120A FRMD8ATP5G3 PTK7 LOC441454 /// PPP1R3B LOC728026 /// PTMA /// PTMAP5 KPNB1C12orf65 COX4I1 1565862_PM_a_at CTNNB1 238712_PM_at GGCX 243682_PM_atALKBH3 SPNS1 TTC39C RECK STAG2 DHX37 ZNF625 TCTN3 PNKD 236370_PM_atC20orf103 CAST FAM113A 240038_PM_at VDAC3 TAB2 RAMP1 ZNF638 SLC25A12METRN 236322_PM_at IRS2 MRP63 TCEAL8 UBE2H ANAPC5 BAT2L2 239479_PM_x_at236944_PM_at PDSS2 230868_PM_at GLS2 CHD2 240103_PM_at PPP2R2B238812_PM_at SPTLC2 LMO7 NEDD8 ZNF33A PRRG3 MYST3 RANGRF MPP5 FAR1C7orf53 1561202_PM_at FOXP1 MRPS11 ZNF207 TOMM40L CDC14A 237072_PM_atNOC4L CBX6 MAPKAPK5 PAPD4 PTPLB MVP LOC100506245 CC2D1A 1556332_PM_atLASS5 EIF2AK3 GIPC1 MYCL1 229255_PM_x_at ACSL3 SELPLG EXOSC7 LYPLAL1HSCB PICALM ATP5F1 CDV3 MLKL ZNF581 LOC100272228 LOC100505592 TBC1D9NDUFA11 PRKCB LDOC1L LOC728825 /// SUMO2 ATXN10 TAX1BP3 GBF1LOC100130522 SIT1 LSM12 240176_PM_at RSU1 GSTM1 STXBP3 TRADD RNF24ZNF207 RPS19BP1 220912_PM_at 242868_PM_at RASGRP2 PLEKHJ1 RPL361558154_PM_at ST20 SELK HSD17B8 SUGP1 1557551_PM_at 243305_PM_at SEL1L220809_PM_at CANX MXD1 FCGR2C PSMA1 PRPF4B CDYL PHF15 ZNRF1 NCRNA00152ASAP1 234150_PM_at RRS1 LRCH4 /// SAP25 RABGGTB COBLL1 PDE5A IL2RA ESR2STYXL1 GZF1 SUDS3 C14orf109 SRSF6 TIMM9 TFB1M EPCI 1556944_PM_at213979_PM_s_at MIAT PDCD7 DDX24 TTC27 STX16 SSTR2 AVIL BHLHE40 PRKAB11569477_PM_at SEC14L2 FGD4 UFSP2 CNPY2 PRO0471 RPL15 SAMD4B DDX42LOC553103 NLRP1 1566825_PM_at PDHB CHD4 24076 l_PM_at APC COX4I1 KCNJ2BZW2 GDPD3 EEF1D C7orf41 1556195_PM_a_at HBG1 /// HBG2 LOC339352 PTPN2BCAS2 CD300A 226252_PM_at 239048_PM_at SOS2 SCUBE3 PARK7 232595_PM_atNCRNA00107 PALLD CPT2 RFK GAS7 SIPA1L2 USP40 ACAP2 DTHD1 ZNF615 SNRNP25PGGT1B CDK2AP1 PICALM TSC22D1 IAH1 ARPP19 HBB GGNBP2 TTC12 ABCA5241613_PM_at LONP2 ABHD5 TROVE2 LOC729013 WDYHV1 ROD1 PSMB4 PRKCDTCP11L1 CHFR PGS1 UGCG IL13RA1 1556646_PM_at C16orf13 213048_PM_s_at243931_PM_at 230599_PM_at C4orf45 1560474_PM_at FLJ38717 PEX11A NUDT19NOTCH2 229370_PM_at TRBC2 241445_PM_at LOC100128439 KIAA0141 SEMA4FLOC115110 SRGAP2P1 SLC6A13 ARPP21 1560738_PM_at ROCK1 ACP1 NFATC2 CDC16GDE1 SYNGR2 RNPC3 RHOB DOCK8 ZNF207 POC1B ERGIC1 CEP72 CUL2 GORASP2OR2W3 WHAMML1 /// RASGRP2 239603_PM_x_at C1orf220 WHAMML2 TMSB4X ///TMSL3 ATG13 DHX37 235680_PM_at NFYA C3orf37 TWF1 UBE3C 239709_PM_at PHAX232685_PM_at NACA 237626_PM_at KDELR1 LOC100507596 SLC6A6 SH3BP2243089_PM_at 239597_PM_at RIOK2 235894_PM_at 243482_PM_at BCL10241860_PM_at SSBP4 CCDC50 WIPF2 C10orf76 PRELID1 CD68 230324_PM_at1561128_PM_at MDM2 HYLS1 232330_PM_at LIG3 RNF181 CACNA2D3 TBC1D8 MRPS31CUL4B ZNF43 MGAT2 243249_PM_at DOLK LOC91548 NXT1 ZFAND5 TOR1AIP21566501_PM_at TNRC6B DERA CNIH4 SNRPA1 PER1 TMEM159 MAX PNPO TMEM43SCGB1C1 244592_PM_at IL6ST 224989_PM_at ANO6 SMARCAL1 C15orf63 TM6SF2233506_PM_at 237554_PM_at DNAJB11 FNTA UNKL NDUFB4 MESDC1 AP4M1 GAB2ACBD6 PPP1R14B MRC2 1570621_PM_at 238743_PM_at WTAP FNDC3B SRGAP2 RQCD1HSPC157 ADAMTS1 LOC151657 RBBP6 TRA2A DSC2 PTPRE C17orf77 C14orf119234753_PM_x_at MPV17 TRIB3 PPP1R16B 1558418_PM_at C17orf108 236781_PM_atSLC25A5 YY1 240154_PM_at C15orf63 /// SERF2 MEX3D VEGFB PRMT1 C6orf226DDOST AMBRA1 216490_PM_x_at COX6B1 KLHL22 CCT6A CSNK1A1 ARID2232344_PM_at C4orf21 HDDC2 CD97 VNN3 MGA MRPL33 LRCH4 CLEC4E SAP30ORMDL2 APBA3 ANKRD57 C20orf72 TNFSF12-TNFSF13 /// TNFSF13 FBXO41 ERICH1/// FLJ00290 IQCK RFX7 242772_PM_x_at SRSF2IP VIL1 ZNF397 C10orf76DPYSL5 244502_PM_at PTPRC IK /// TMCO6 ANPEP ATF6B /// TNXB PARP8ATP6AP2 ADAM17 214848_PM_at C18orf21 LOC727820 SRD5A3 PREX1 ZEB2 PIK3CDCCDC12 LSM14B STK35 AIP SRC NIP7 244548_PM_at UBAC2 242440_PM_at PPA2ASAH1 MED23 USP42 PLIN5 ZNF552 EI24 MIB2 NAMPT CAND1 NME3 GDF10 RAPGEF2LOC647979 1559663_PM_at RNPC3 SNX2 KIAA2026 237264_PM_at CCDC154 WIBGSLC12A6 WAC DPH2 ZBED1 C11orf10 C17orf37 P4HB TRO GRIN2B 1560622_PM_atCSNK1G2 TPSAB1 HNRNPA3 /// HNRNPA3P1 232876_PM_at ATXN7 SNX20 BAZ2A1566966_PM_at PITPNM1 KRTAP9-2 CISH ZNF362 PDLIM1 C17orf63 NSMAF239555_PM_at KIAA1143 SUPT4H1 LETM1 RASSF7 KLF6 PRKAA1 235596_PM_atC17orf81 235685_PM_at TRIM23 ADORA3 224082_PM_at SORD ATP11B RSBN1 PALB2MALAT1 ACTG1 233354_PM_at 239819_PM_at TMEM107 PIGC STAG2 LOC200772 PER1GRAMD1A CD300LB PCBP2 NBR1 SNUPN 1564996_PM_at BTF3 RIBC1 FLJ44342 PHF1LOC221442 234255_PM_at SMU1 SEMA7A 239893_PM_at IFT27 LOC1001348221557772_PM_at WDR1 IMPAD1 229206_PM_at WDFY3 SMYD3 IL17RA /// LOC150166IDH3A 242016_PM_at 242384_PM_at LOC100190986 FCHO2 231039_PM_at MGEA5SNRK GHITM RNF213 CTDNEP1 ASTE1 ELP3 EML4 210598_PM_at KIAA1659 SUSD1HIPK3 1562898_PM_at 231471_PM_at STYXL1 C14orf1 ALKBH7 RAC2 URB1242688_PM_at XAB2 C9orf89 1556645_PM_s_at CD44 DUSP23 ZNF416 EXOSC3WBSCR16 PRKAR1A ZNF599 TRD@ 237683_PM_s_at PDSS1 CCNH CDYL SLC14A1 SAP18FAM190B ITPKC GNAS CIRBP SLC16A3 NUDT2 C4orf23 MED19 229968_PM_atXRCC6BP1 DUSP10 LOC644613 RPRD1A 241501_PM_at 1564886_PM_at PDK3 WSB1C5orf20 MBD1 RBM5 USP15 RNMTL1 KLHDC7B

TABLE 2 Inclusion/Exclusion criteria General Inclusion Criteria 1) Adultkidney transplant (age >18 years): first or multiple transplants, highor low risk, cadaver or living donor organ recipients. 2) Any cause ofend-stage renal disease except as described in Exclusions. 3) Consent toallow gene expression and proteomic studies to be done on samples. 4)Meeting clinical and biopsy criteria specified below for Groups 1-3.General Exclusion Criteria 1) Combined organ recipients:kidney/pancreas, kidney/islet, heart/kidney and liver/kidney. 2) Arecipient of two kidneys simultaneously unless the organs are both adultand considered normal organs (rationale is to avoid inclusion ofpediatric en bloc or dual adult transplants with borderline organs). 3)Any technical situation or medical problem such as a known bleedingdisorder in which protocol biopsies would not be acceptable for safetyreasons in the best judgment of the clinical investigators. 4) Patientswith active immune-related disorders such as rheumatoid arthritis, SLE,scleroderma and multiple sclerosis. 5) Patients with acute viral orbacterial infections at the time of biopsy. 6) Patients with chronicactive hepatitis or HIV. 7) CAN, that at the time of identification arein the best judgment of the clinicians too far along in the process orprogressing to rapidly to make it likely that they will still have afunctioning transplant a year later. 8) Patients enrolled in anotherresearch study that in the best judgment of the clinical centerinvestigator involves such a radical departure from standard therapythat the patient would not be representative of the groups under studyin the Program Project. Acute Rejection (AR) Specific 1) Clinicalpresentation with acute kidney transplant dysfunction at any timeInclusion Criteria post transplant a. Biopsy-proven AR withtubulointerstitial cellular rejection with or without acute vascularrejection Acute Rejection (AR) Specific 1) Evidence of concomitant acuteinfection Exclusion Criteria a. CMV b. BK nephritis c. Bacterialpyelonephritis d. Other 2) Evidence of anatomical obstruction orvascular compromise 3) If the best judgment of the clinical team priorto the biopsy is that the acute decrease in kidney function is due todehydration, drug effect (i.e. ACE inhibitor) or calcineurin inhibitorexcess 4) If the biopsy is read as drug hypersensitivity (i.e.sulfa-mediated interstitial nephritis) 5) Evidence of hemolytic uremicsyndrome Well-functioning Transplant/No 1) Patient between 12 and 24months post transplant Rejection (TX) Specific Inclusion 2) Stable renalfunction defined as at least three creatinine levels over a threeCriteria month period that do not change more than 20% and without anypattern of a gradual increasing creatinine. 3) No history of rejectionor acute transplant dysfunction by clinical criteria or previous biopsy4) Serum creatinines <1.5 mg/dL for women, <1.6 mg/dL for men 5) Theymust also have a calculated or measured creatinine clearance >45 ml/minute 6) They must have well controlled blood pressure definedaccording to the JNC 7 guidelines of <140/90 (JNC7 Express, The SeventhReport of the Noint National Committee on Prevention, Detection,Evaluation and Treatment of High Blood Pressure, NIH Publication No.03-5233, December 2003) Well-functioning Transplant/No 1) Patient lessthan one month after steroid withdrawal Rejection (TX) SpecificExclusion 2) Patients with diabetes (Type I or II, poorly controlled)Criteria 3) Evidence of concomitant acute infection a. CMV b. BKnephritis c. Bacterial pyelonephritis *A special note regarding whynoncompliance is not an exclusion criterion is important to emphasize.Noncompliance is not a primary issue in determining gene expression andproteomics profiles associated with molecular pathways of transplantimmunity and tissue injury/repair.

TABLE 3 Clinical characteristics for the 148 study samples. MultivariateMultivariate Analysis^(¶) Analysis^(¶) Significance All Study SamplesSignificance (Phenotypes/ TX AR ADNR Significance* (Phenotypes) Cohorts)Subject Numbers 45 64 39 — — — Recipient Age ± SD^(§) 50.1 ± 14.5 44.9 ±14.3 49.7 ± 14.6 NS{circumflex over ( )} NS NS (Years) % FemaleRecipients 34.8 23.8 20.5 NS NS NS % Recipient African 6.8 12.7 12.8 NSNS NS American % Pre-tx Type II 25.0 17.5 21.6 NS NS NS Diabetes %PRA >20 29.4 11.3 11.5 NS NS NS HU Mismatch ± SD 4.2 ± 2.1 4.3 ± 1.6 3.7± 2.1 NS NS NS % Deceased Donor 43.5 65.1 53.8 NS NS NS Donor Age ± SD40.3 ± 14.5 38.0 ± 14.3 46.5 ± 14.6 NS NS NS (Years) % Female Donors37.0 50.8 46.2 NS NS NS % Donor African 3.2 4.9 13.3 NS NS NS American %Delayed Graft 19.0 34.4 29.0 NS NS NS Function % Induction 63.0 84.182.1 NS NS NS Serum Creatinine ± SD 1.5 ± 0.5 3.2 ± 2.8 2.7 ± 1.8 TX vs.AR = 0.00001 TX vs. AR = 0.04 TX vs. AR vs. (mg/dL) TX vs. ADNR = 0.0002TX vs. ADNR = 0.01 ADNR = 0.00002 AR vs. ADNR = NS AR vs. ADNR = NS Timeto Biopsy ± SD  512 ± 1359  751 ± 1127 760 ± 972 NS NS NS (Days) Biopsy≤365 days (%) 27 38 23 NS NS NS (54.2%) (49.0%) (52.4%) Biopsy >366 days(%) 19 32 18 NS NS NS (45.8%) (51.0%) (47.6%) % Calcineurin 89.7 94.081.1 NS NS NS Inhibitors % Mycophenolic Acid 78.3 85.7 84.6 NS NS NSDerivatives % Oral Steroids 26.1 65.1 74A TX vs. AR = 0.001 TX vs. ADNR= 0.04 NS TX vs. ADNR = 0.001 C4d Positive Staining 0/13 12/36 1/20 NSNS NS (%)^(§) (0%) (33.3%) (5%) *Significance for at comparisons weredetermined with paired Students t-test for pair-wise comparisons of datawith Standard Deviations and for dichotomous data comparisons byChi-Square. ^(¶)A multivariate logistic regression model was used with aWald test correction. In the first analysis (Phenotypes) we used all 148samples and in the second analysis (Phenotypes/Cohorts) we did theanalysis for each randomized set of 2 cohorts (Discovery andValidation). {circumflex over ( )}NS = not significant (p ≥ 0.05)^(§)Subjects with biopsy-positive staining for C4d and total number ofsubjects whose biopsies were stained for C4d with (%).

TABLE 4 Diagnostic metrics for the 3-way Nearest Centroid classifiersfor AR, ADNR and TX in Discovery and Validation Cohorts % % PredictivePredictive Positive Negative Positive Negative Accuracy Accuracy Sensi-Speci- Predictive Predictive Sensi- Speci- Predictive Predictive(Discovery (Validation tivity ficity Value Value tivity ficity ValueValue Method Classifies Cohort) Cohort) (%) (X) (%) (%) AUC (%) (%) (%)(%) AUC 200 TX vs. AR 92% 83% 87% 96%  95% 89% 0.917 73% 92% 89% 79%0.837 Classifiers TX vs. 91% 82% 95% 90%  91% 95% 0.913 89% 76% 76% 89%0.817 ADNR AR vs. 92% 90% 87% 100% 100% 86% 0.933 89% 92% 89% 92% 0.893ADNR 100 TX vs. AR 91% 83% 87% 93%  91% 90% 0.903 76% 88% 84% 82% 0.825Classifiers TX vs. 98% 81% 95% 100% 100% 95% 0.975 84% 79% 80% 83% 0.814ADNR AR vs. 98% 90% 95% 100% 100% 97% 0.980 88% 92% 88% 92% 0.900 ADNR50 TX vs. AR 92% 94% 88% 96%  95% 90% 0.923 88% 91% 89% 89% 0.891Classifiers TX vs. 94% 95% 92% 98%  98% 90% 0.944 92% 90% 88% 89% 0.897ADNR AR vs. 97% 93% 95% 97% 100% 97% 0.969 89% 91% 89% 89% 0.893 ADNR 25TX vs. AR 89% 92% 81% 96%  95% 84% 0.890 88% 90% 90% 89% 0.894Classifiers TX vs. 95% 95% 95% 95%  95% 95% 0.948 92% 92% 89% 89% 0.898ADNR AR vs. 96% 91% 95% 96%  95% 96% 0.955 85% 90% 89% 88% 0.882 ADNR

TABLE 5 Diagnostic metrics for the 3-way DLDA and SVM classifiers forAR, ADNR and TX in Discovery and Validation Cohorts % % PredictivePredictive Positive Negative Positive Negative Accuracy Accuracy Sensi-Speci- Predictive Predictive Sensi- Speci- Predictive Predictive(Discovery (Validation tivity ficity Value Value tivity ficity ValueValue Method Classifies Cohort) Cohort) (%) (%) (%) (%) AUC (%) (%) (%)(%) AUC 200 Classifiers DLDA TX vs. AR  90%  84%  87%  93%  91%  89%0.896  76%  92%  89%  81% 0.845 TX vs.  95%  82%  95%  94%  95%  95%0.945  89%  76%  76%  89% 0.825 ADNR AR vs.  92%  84%  83% 100% 100% 86% 0.923  84%  92%  89%  88% 0.880 ADNR SVM TX vs. AR 100%  83% 100%100% 100% 100% 1.000  82%  86%  81%  86% 0.833 TX vs. 100%  96% 100%100% 100% 100% 1.000 100%  95%  95% 100% 0.954 ADNR AR vs. 100%  95%100% 100% 100% 100% 1.000  95%  96%  95%  96% 0.954 ADNR 100  84%  82%0.825 Classifiers DLDA TX vs. AR  91%  83%  88%  93%  91%  90% 0.905 76%  88%  80%  83% 0.815 TX vs.  97%  82%  95% 100% 100%  95% 0.970 84%  79% ADNR AR vs.  98%  90%  95% 100% 100%  97% 0.980  88%  92%  88% 92% 0.900 ADNR SVM TX vs. AR  97%  87%  88% 100% 100%  91% 0.971  83% 92%  90%  85% 0.874 TX vs.  98%  86%  96% 100% 100%  95% 0.988  86% 88%  90%  83% 0.867 ADNR AR vs. 100%  87% 100% 100% 100% 100% 1.000 83%  92%  88%  88% 0.875 ADNR 50 Classifiers DLDA TX vs. AR  93%  83% 88%  97%  96%  90% 0.927  78%  88%  86%  81% 0.832 TX vs.  96%  84% 92% 100% 100%  90% 0.955  82%  87%  90%  76% 0.836 ADNR AR vs.  98% 85%  95% 100% 100%  97% 0.979  81%  88%  81%  88% 0.845 ADNR SVM TX vs.AR  95%  83%  88% 100% 100%  91% 0.946  77%  88%  87%  79% 0.827 TX vs. 98%  92%  96% 100% 100%  95% 0.976  87% 100% 100%  81% 0.921 ADNR ARvs. 100%  85% 100% 100% 100% 100% 1.000  87%  85%  77%  92% 0.852 ADNR25 Classifiers DLDA TX vs. AR  88%  92%  83%  93%  90%  88% 0.884  88% 85%  78%  90% 0.852 TX vs.  92%  93%  95%  90%  90%  95% 0.924  92% 88%  85%  81% 0.864 ADNR AR vs. 100%  91% 100% 100% 100% 100% 1.000 85%  87%  88%  76% 0.841 ADNR SVM TX vs. AR  95%  92%  92%  97%  96% 94% 0.945  84%  87%  88%  84% 0.857 TX vs.  96% 100%  96%  95%  96% 95% 0.955 100%  85%  83%  81% 0.874 ADNR AR vs. 100% 100% 100% 100%100% 100% 1.000 100%  86%  82%  81% 0.873 ADNR DLDA—Diagonal linearDiscriminant Analysis SVM—Support Vector Machines

TABLE 6 Optimism-corrected Area Under the Curves (AUC's) comparing twomethods for creating and validating 3-Way classifiers for AR vs. ADNRvs. TX that demonstrates they provide equivalent results. DiscoveryCohort-based 200 probeset classifier* Optimism Corrected MethodClassifies Original AUC Optimism AUC Nearest Centroid AR, TX, ADNR0.8500 0.0262 0.8238 Diagonal Linear Discriminant Analysis AR, TX, ADNR0.8441 0.0110 0.8331 Support Vector Machines AR, TX, ADNR 0.8603 0.01720.8431 Full study sample-based 200 probeset classifier* OptimismOriginal AUC Corrected Method Classifies (Bootstrapping) Optimism AUCNearest Centroid AR, TX, ADNR 0.8641 0.0122 0.8519 Diagonal LinearDiscriminant Analysis AR, TX, ADNR 0.8590 0.0036 0.8554 Support VectorMachines AR, TX, ADNR 0.8669 0.0005 0.8664 *153/200 (77%) of thediscovery cohort-based classifier probesets were in the Top 500 of thefull study sample-based 200 probeset classifier. Similarly, 141/200(71%) of the full study sample-based 200 probeset classifier was in thetop 500 probesets of the discovery cohort-based classifier.

Example 2

Materials and Methods

This Example describes some of the materials and methods employed inidentification of differentially expressed genes in SCAR.

The discovery set of samples consisted of the followingbiopsy-documented peripheral blood samples. 69 PAXgene whole bloodsamples were collected from kidney transplant patients. The samples thatwere analyzed comprised 3 different phenotypes: (1) Acute Rejection (AR;n=21); (2) Sub-Clinical Acute Rejection (SCAR; n=23); and (3) TransplantExcellent (TX; n=25). Specifically, SCAR was defined by a protocolbiopsy done on a patient with totally stable kidney function and thelight histology revealed unexpected evidence of acute rejection (16“Borderline”, 7 Banff 1A). The SCAR samples consisted of 3 month and 1year protocol biopsies, whereas the TXs were predominantly 3 monthprotocol biopsies. All the AR biopsies were “for cause” where clinicalindications like a rise in serum creatinine prompted the need for abiopsy. All patients were induced with Thymoglobulin.

All samples were processed on the Affymetrix HG-U133 PM only pegmicroarrays. To eliminate low expressed signals we used a signal filtercut-off that was data dependent, and therefore expression signals <Log₂3.74 (median signals on all arrays) in all samples were eliminatedleaving us with 48734 probe sets from a total of 54721 probe sets. Weperformed a 3-way ANOVA analysis of AR vs. ADNR vs. TX. This yieldedover 6000 differentially expressed probesets at a p-value <0.001. Evenwhen a False Discovery rate cut-off of (FDR <10%), was used it gave usover 2700 probesets. Therefore for the purpose of a diagnostic signaturewe used the top 200 differentially expressed probe sets (Table 8) tobuild predictive models that could differentiate the three classes. Weused three different predictive algorithms, namely Diagonal LinearDiscriminant Analysis (DLDA), Nearest Centroid (NC) and Support VectorMachines (SVM) to build the predictive models. We ran the predictivemodels using two different methodologies and calculated the Area Underthe Curve (AUC). SVM, DLDA and NC picked classifier sets of 200, 192 and188 probesets as the best classifiers. Since there was very littledifference in the AUC's we decided to use all 200 probesets asclassifiers for all methods. We also demonstrated that these resultswere not the consequence of statistical over-fitting by using thereplacement method of Harrell to perform a version of 1000-testcross-validation. Table 7 shows the performance of these classifier setsusing both one-level cross validation as well as the Optimism CorrectedBootstrapping (1000 data sets).

An important point here is that in real clinical practice the challengeis actually not to distinguish SCAR from AR because by definition onlyAR presents with a significant increase in baseline serum creatinine.The real challenge is to take a patient with normal and stablecreatinine and diagnose the hidden SCAR without having to depend oninvasive and expensive protocol biopsies that cannot be done frequentlyin any case. Though we have already successfully done this using our3-way analysis, we also tested a 2-way prediction of SCAR vs. TX. Thepoint was to further validate that a phenotype as potentially subtleclinically as SCAR can be truly distinguished from TX. At a p-value<0.001, there were 33 probesets whose expression signals highlydifferentiated SCAR and TX, a result in marked contrast with the >2500probesets differentially expressed between AR vs. TX at that samep-value. However, when these 33 probesets (Table 9) were used in NC topredict SCAR and TX creating a 2-way classifier, the predictiveaccuracies with a one-level cross-validation was 96% and with theHarrell 1000 test optimism correction it was 94%. Thus, we are confidentthat we can distinguish SCAR, TX and AR by peripheral blood geneexpression profiling using this proof of principle data set.

TABLE 7 Blood Expression Profiling of Kidney Transplants: 3-WayClassifier AR vs. SCAR vs. TX. Postive Negative Predictive PredictivePredictive AUC after Accuracy Sensitivity Specificity Value ValueAlgorithm Predictors Comparison Thresholding (%) (%) (%) (%) (%) NearestCentroid 200 SCAR vs. TX 1.000 100 100 100 100 100 Nearest Centroid 200SCAR vs. AR 0.953 95 92 100 100 90 Nearest Centroid 200 AR vs. TX 0.93293 96 90 92 95

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. Although any methods and materials similaror equivalent to those described herein can be used in the practice ortesting of the present invention, the preferred methods and materialsare described.

All publications, GenBank sequences, ATCC deposits, patents and patentapplications cited herein are hereby expressly incorporated by referencein their entirety and for all purposes as if each is individually sodenoted. Improvements in kidney transplantation have resulted insignificant reductions in clinical acute rejection (AR) (8-14%)(Meier-Kriesche et al. 2004, Am J Transplant, 4(3): 378-383). However,histological AR without evidence of kidney dysfunction (i.e. subclinicalAR) occurs in >15% of protocol biopsies done within the first year.Without a protocol biopsy, patients with subclinical AR would be treatedas excellent functioning transplants (TX). Biopsy studies also documentsignificant rates of progressive interstitial fibrosis and tubularatrophy in >50% of protocol biopsies starting as early as one year posttransplant.

TABLE 8 200 Probeset classifer for distinguishing AR, SCAR and TX basedon a 3-way ANOVA stepup AR- SCAR- TX- p-value p-value Mean Mean Mean #Probeset ID Gene Symbol Gene Title (Phenotype) (Phenotype) Signal SignalSignal  1 238108_PM_at — — 1.70E−10 8.27E−06 73.3 45.4 44.4  2243524_PM_at — — 3.98E−10 9.70E−06 72.3 41.3 37.7  3 1558831_PM_x_at — —5.11E−09 8.30E−05 48.1 30.8 31.4  4 229858_PM_at — — 7.49E−09 8.31E−05576.2 359.3 348.4  5 236685_PM_at — — 8.53E−09 8.31E−05 409.1 213.3211.0  6 213546_PM_at DKFZP586l1420 hypothetical protein 3.52E−082.60E−04 619.2 453.7 446.0 DKFZp586l1420  7 231958_PM_at C3orf31Chromosome 3 open 4.35E−08 2.60E−04 22.8 20.1 16.4 reading frame 31  8210275_PM_s_at ZFAND5 zinc finger, AN1-type 4.96E−08 2.60E−04 1045.91513.6 1553.8 domain 5  9 244341_PM_at — — 5.75E−08 2.60E−04 398.3 270.7262.8  10 1558822_PM_at — — 5.84E−08 2.60E−04 108.6 62.9 56.8  11242175_PM_at — — 5.87E−08 2.60E−04 69.1 37.2 40.0  12 222357_PM_atZBTB20 zinc finger and BTB 6.97E−08 2.83E−04 237.4 127.4 109.8 domaincontaining 20  13 206288_PM_at PGGT1B protein 9.42E−08 3.53E−04 20.834.7 34.2 geranylgeranyltransferase type I, beta subunit  14222306_PM_at — — 1.03E−07 3.59E−04 23.3 15.8 16.0  15 1569601_PM_at — —1.67E−07 4.80E−04 49.5 34.1 29.7  16 235138_PM_at — — 1.69E−07 4.80E−041169.9 780.0 829.7  17 240452_PM_at GSPT1 G1 to S phase 1.74E−074.80E−04 97.7 54.4 48.6 transition 1  18 243003_PM_at — — 1.77E−074.80E−04 92.8 52.5 51.3  19 218109_PM_s_at MFSD1 major facilitator1.90E−07 4.87E−04 1464.0 1881.0 1886.4 superfamily domain containing 1 20 241681_PM_at — — 2.00E−07 4.87E−04 1565.7 845.7 794.6  21243878_PM_at — — 2.19E−07 5.08E−04 76.1 39.7 39.5  22 233296_PM_x_at — —2.33E−07 5.17E−04 347.7 251.5 244.7  23 243318_PM_at DCAF8 DDB1 and CUL42.52E−07 5.34E−04 326.2 229.5 230.2 associated factor 8  24 236354_PM_at— — 3.23E−07 6.39E−04 47.1 31.2 27.8  25 243768_PM_at — — 3.35E−076.39E−04 1142.0 730.6 768.5  26 238558_PM_at — — 3.65E−07 6.39E−04 728.5409.4 358.4  27 237825_PM_x_at — — 3.66E−07 6.39E−04 34.2 20.9 19.9  28244414_PM_at — — 3.67E−07 6.39E−04 548.7 275.2 284.0  29 215221_PM_at —— 4.06E−07 6.83E−04 327.2 176.7 171.9  30 235912_PM_at — — 4.46E−077.25E−04 114.1 71.4 59.5  31 239348_PM_at — — 4.87E−07 7.54E−04 20.114.5 13.4  32 240499_PM_at — — 5.06E−07 7.54E−04 271.4 180.1 150.2  33208054_PM_at HERC4 hect domain and RLD 4 5.11E−07 7.54E−04 114.9 57.660.0  34 240263_PM_at — — 5.46E−07 7.81E−04 120.9 78.7 66.6  35241303_PM_x_at — — 5.78E−07 7.81E−04 334.5 250.3 261.5  36 233692_PM_at— — 5.92E−07 7.81E−04 22.4 15.5 15.0  37 243561_PM_at — — 5.93E−077.81E−04 341.1 215.1 207.3  38 232778_PM_at — — 6.91E−07 8.86E−04 46.531.0 28.5  39 237632_PM_at — — 7.09E−07 8.86E−04 108.8 61.0 57.6  40233690_PM_at — — 7.30E−07 8.89E−04 351.1 222.7 178.1  41 220221_PM_atVPS13D vacuolar protein 7.50E−07 8.89E−04 93.5 60.0 59.9 sorting 13homolog D (S. cerevisiae)  42 242877_PM_at — — 7.72E−07 8.89E−04 173.8108.1 104.0  43 218155_PM_x_at TSR1 TSR1, 20S rRNA 7.86E−07 8.89E−04217.2 165.6 164.7 accumulation, homolog (S. cerevisiae)  44239603_PM_x_at — — 8.24E−07 8.89E−04 120.9 75.5 81.1  45 242859_PM_at —— 8.48E−07 8.89E−04 221.1 135.4 138.3  46 240866_PM_at — — 8.54E−078.89E−04 65.7 33.8 35.2  47 239661_PM_at — — 8.72E−07 8.89E−04 100.548.3 45.2  48 224493_PM_x_at C18orf45 chromosome 18 8.77E−07 8.89E−04101.8 78.0 89.7 open reading frame 45  49 1569202_PM_x_at — — 8.98E−078.89E−04 23.3 18.5 16.6  50 1560474_PM_at — — 9.12E−07 8.89E−04 25.217.8 18.5  51 232511_PM_at — — 9.48E−07 9.06E−04 77.2 46.1 49.9  52228119_PM_at LRCH3 leucine-rich repeats 1.01E−06 9.51E−04 117.2 84.276.1 and calponin homology (CH) domain containing 3  53 228545_PM_atZNF148 zinc finger protein 148 1.17E−06 9.99E−04 789.9 571.1 579.7  54232779_PM_at — — 1.17E−06 9.99E−04 36.7 26.0 20.7  55 239005_PM_atFLJ39739 Hypothetical 1.18E−06 9.99E−04 339.1 203.7 177.7 FLJ39739  56244478_PM_at LRRC37A3 leucine rich repeat 1.20E−06 9.99E−04 15.7 12.612.7 containing 37, member A3  57 244535_PM_at — — 1.28E−06 9.99E−04261.5 139.5 137.8  58 1562673_PM_at — — 1.28E−06 9.99E−04 77.4 46.5 51.8 59 240601_PM_at — — 1.29E−06 9.99E−04 212.6 107.7 97.7  60 239533_PM_atGPR155 G protein-coupled 1.30E−06 9.99E−04 656.3 396.7 500.1 receptor155  61 222358_PM_x_at — — 1.32E−06 9.99E−04 355.2 263.1 273.7  62214707_PM_x_at ALMS1 Alstrom syndrome 1 1.32E−06 9.99E−04 340.2 255.9266.0  63 236435_PM_at — — 1.32E−06 9.99E−04 144.0 92.6 91.1  64232333_PM_at — — 1.33E−06 9.99E−04 487.7 243.7 244.3  65 222366_PM_at —— 1.33E−06 9.99E−04 289.1 186.1 192.8  66 215611_PM_at TCF12transcription factor 12 1.38E−06 1.02E−03 45.5 32.4 30.8  671558002_PM_at STRAP Serine/threonine 1.40E−06 1.02E−03 199.6 146.7 139.7kinase receptor associated protein  68 239716_PM_at — — 1.43E−061.02E−03 77.6 49.5 45.5  69 239091_PM_at — — 1.45E−06 1.02E−03 76.9 44.045.0  70 238883_PM_at — — 1.68E−06 1.15E−03 857.1 475.5 495.1  71235615_PM_at PGGT1B protein 1.72E−06 1.15E−03 127.0 235.0 245.6geranylgeranyltransferase type I, beta subunit  72 204055_PM_s_at CTAGE5CTAGE family, 1.77E−06 1.15E−03 178.8 115.2 105.9 member 5  73239757_PM_at ZFAND6 Zinc finger, AN1-type 1.81E−06 1.15E−03 769.6 483.3481.9 domain 6  74 1558409_PM_at — — 1.82E−06 1.15E−03 14.8 10.9 11.8 75 242688_PM_at — — 1.85E−06 1.15E−03 610.5 338.4 363.4  76242377_PM_x_at THUMPD3 THUMP domain 1.87E−06 1.15E−03 95.5 79.0 81.3containing 3  77 242650_PM_at — — 1.88E−06 1.15E−03 86.0 55.5 47.4  78243589_PM_at KIAA1267 /// KIAA1267 /// 1.89E−06 1.15E−03 377.8 220.3210.4 LOC100294337 hypothetical LOC100294337  79 227384_PM_s_at — —1.90E−06 1.15E−03 3257.0 2255.5 2139.7  80 237864_PM_at — — 1.91E−061.15E−03 121.0 69.2 73.4  81 243490_PM_at — — 1.92E−06 1.15E−03 24.617.5 16.5  82 244383_PM_at — — 1.96E−06 1.17E−03 141.7 93.0 77.5  83215908_PM_at — — 2.06E−06 1.19E−03 98.5 67.9 67.5  84 230651_PM_at — —2.09E−06 1.19E−03 125.9 74.3 71.5  85 1561195_PM_at — — 2.14E−061.19E−03 86.6 45.1 43.9  86 239268_PM_at NDUFS1 NADH 2.14E−06 1.19E−0314.0 12.0 11.3 dehydrogenase (ubiquinone) Fe—S protein 1, 75 kDa(NADH-coenzyme Q reductase)  87 236431_PM_at SR140 U2-associated SR1402.16E−06 1.19E−03 69.4 47.9 43.9 protein  88 236978_PM_at — — 2.19E−061.19E−03 142.4 88.6 88.1  89 1562957_PM_at — — 2.21E−06 1.19E−03 268.3181.8 165.4  90 238913_PM_at — — 2.21E−06 1.19E−03 30.9 20.2 20.1  91239646_PM_at — — 2.23E−06 1.19E−03 100.3 63.1 60.8  92 235701_PM_at — —2.34E−06 1.24E−03 133.2 66.1 60.0  93 235601_PM_at — — 2.37E−06 1.24E−03121.9 75.5 79.0  94 230918_PM_at — — 2.42E−06 1.25E−03 170.4 114.5 94.4 95 219112_PM_at FNIP1 /// folliculin interacting 2.49E−06 1.28E−03568.2 400.2 393.4 RAPGEF6 protein 1 /// Rap guanine nucleotide exchangefactor (GEF) 6  96 202228_PM_s_at NPTN neuroplastin 2.52E−06 1.28E−031017.7 1331.5 1366.4  97 242839_PM_at — — 2.78E−06 1.39E−03 17.9 14.013.6  98 244778_PM_x_at — — 2.85E−06 1.42E−03 105.1 68.0 65.9  99237388_PM_at — — 2.91E−06 1.42E−03 59.3 38.0 33.0 100 202770_PM_s_atCCNG2 cyclin G2 2.92E−06 1.42E−03 142.2 269.0 270.0 101 240008_PM_at — —2.96E−06 1.42E−03 96.2 65.6 56.2 102 1557718_PM_at PPP2R5C proteinphosphatase 2.97E−06 1.42E−03 615.2 399.8 399.7 2, regulatory subunitB′, gamma 103 215528_PM_at — — 3.01E−06 1.42E−03 126.8 62.6 69.0 104204689_PM_at HHEX hematopoietically 3.08E−06 1.44E−03 381.0 499.9 567.9105 213718_PM_at RBM4 expressed homeobox 3.21E−06 1.46E−03 199.3 140.6132.2 RNA binding motif protein 4 106 243233_PM_at — — 3.22E−06 1.46E−03582.3 343.0 337.1 107 239597_PM_at — — 3.23E−06 1.46E−03 1142.9 706.6720.8 108 232890_PM_at — — 3.24E−06 1.46E−03 218.0 148.7 139.9 109232883_PM_at — — 3.42E−06 1.53E−03 127.5 79.0 73.1 110 241391_PM_at — —3.67E−06 1.62E−03 103.8 51.9 48.3 111 244197_PM_x_at — — 3.71E−061.62E−03 558.0 397.3 418.8 112 205434_PM_s_at AAK1 AP2 associated3.75E−06 1.62E−03 495.2 339.9 301.2 kinase 1 113 235725_PM_at SMAD4 SMADfamily 3.75E−06 1.62E−03 147.1 102.1 112.0 member 4 114 203137_PM_atWTAP Wilms tumor 1 3.89E−06 1.66E−03 424.1 609.4 555.8 associatedprotein 115 231075_PM_x_at RAPH1 Ras association 3.91E−06 1.66E−03 30.419.3 18.2 (RalGDS/AF-6) and pleckstrin homology domains 1 116236043_PM_at LOC100130175 hypothetical protein 3.98E−06 1.67E−03 220.6146.2 146.5 LOC100130175 117 238299_PM_at — — 4.09E−06 1.70E−03 217.1130.4 130.3 118 243667_PM_at — — 4.12E−06 1.70E−03 314.5 225.3 232.8 119223937_PM_at FOXP1 forkhead box P1 4.20E−06 1.72E−03 147.7 85.5 90.9 120238666_PM_at — — 4.25E−06 1.72E−03 219.1 148.3 145.5 121 1554771_PM_at —— 4.28E−06 1.72E−03 67.2 41.5 40.8 122 202379_PM_s_at NKTR naturalkiller-tumor 4.34E−06 1.73E−03 1498.2 1170.6 1042.6 recognition sequence123 244695_PM_at GHRLOS ghrelin opposite 4.56E−06 1.79E−03 78.0 53.052.5 strand (non-protein coding) 124 239393_PM_at — — 4.58E−06 1.79E−03852.0 554.2 591.7 125 242920_PM_at — — 4.60E−06 1.79E−03 392.8 220.9251.8 126 242405_PM_at — — 4.66E−06 1.80E−03 415.8 193.8 207.4 1271556432_PM_at — — 4.69E−06 1.80E−03 61.5 43.1 38.1 128 1570299_PM_at — —4.77E−06 1.81E−03 27.0 18.0 19.8 129 225198_PM_at VAPA VAMP (vesicle-4.85E−06 1.83E−03 192.0 258.3 273.9 associated membrane protein)-associated protein A, 33 kDa 130 230702_PM_at — — 4.94E−06 1.85E−03 28.218.4 17.5 131 240262_PM_at — — 5.07E−06 1.88E−03 46.9 22.8 28.0 132232216_PM_at YME1L1 YME1-like 1 (S. 5.14E−06 1.89E−03 208.6 146.6 130.1cerevisiae) 133 225171_PM_at ARHGAP18 Rho GTPase 5.16E−06 1.89E−03 65.9109.1 121.5 activating protein 18 134 243992_PM_at — — 5.28E−06 1.92E−03187.1 116.0 125.6 135 227082_PM_at — — 5.45E−06 1.96E−03 203.8 140.4123.0 136 239948_PM_at NUP153 nucleoporin 153 kDa 5.50E−06 1.96E−03 39.626.5 27.8 137 221905_PM_at CYLD cylindromatosis 5.51E−06 1.96E−03 433.0316.8 315.1 (turban tumor syndrome) 138 242578_PM_x_at SLC22A3 Solutecarrier family 5.56E−06 1.96E−03 148.4 109.2 120.1 22 (extraneuronalmonoamine transporter), member 3 139 1569238_PM_a_at — — 5.73E−061.99E−03 71.0 33.0 36.1 140 201453_PM_x_at RHEB Ras homolog 5.76E−061.99E−03 453.3 600.0 599.0 enriched in brain 141 236802_PM_at — —5.76E−06 1.99E−03 47.9 29.1 29.6 142 232615_PM_at — — 5.82E−06 1.99E−034068.5 3073.4 2907.4 143 237179_PM_at PCMTD2 protein-L- 5.84E−061.99E−03 48.7 30.2 26.8 isoaspartate (D- aspartate) O— methyltransferasedomain containing 2 144 203255_PM_at FBX011 F-box protein 11 5.98E−062.02E−03 748.3 529.4 539.6 145 212989_PM_at SGMS1 sphingomyelin 6.04E−062.03E−03 57.2 93.1 107.9 synthase 1 146 236754_PM_at PPP1R2 proteinphosphatase 6.17E−06 2.05E−03 505.3 380.7 370.1 1, regulatory(inhibitor) subunit 2 147 1559496_PM_at PPA2 pyrophosphatase 6.24E−062.05E−03 68.8 39.7 39.3 (inorganic) 2 148 236494_PM_x_at — — 6.26E−062.05E−03 135.0 91.1 82.9 149 237554_PM_at — — 6.30E−06 2.05E−03 53.431.5 30.1 150 243469_PM_at — — 6.37E−06 2.05E−03 635.2 308.1 341.5 151240155_PM_x_at ZNF493 /// zinc finger protein 6.45E−06 2.05E−03 483.9299.9 316.6 ZNF738 493 /// zinc finger protein 738 152 222442_PM_s_atARL8B ADP-ribosylation 6.47E−06 2.05E−03 201.5 292.6 268.3 factor-like8B 153 240307_PM_at — — 6.48E−06 2.05E−03 55.4 36.8 33.1 154200864_PM_s_at RAB11A RAB11A, member 6.50E−06 2.05E−03 142.1 210.9 233.0RAS oncogene family 155 235757_PM_at — — 6.53E−06 2.05E−03 261.4 185.2158.9 156 222351_PM_at PPP2R1B protein phosphatase 6.58E−06 2.06E−0375.8 51.1 45.4 2, regulatory subunit A, beta 157 222788_PM_s_at RSBN1round spermatid 6.63E−06 2.06E−03 389.9 302.7 288.2 basic protein 1 158239815_PM_at — — 6.70E−06 2.06E−03 216.9 171.4 159.5 159 219392_PM_x_atPRR11 proline rich 11 6.77E−06 2.07E−03 1065.3 827.5 913.2 160240458_PM_at — — 6.80E−06 2.07E−03 414.3 244.6 242.0 161 235879_PM_atMBNL1 Muscleblind-like 6.88E−06 2.08E−03 1709.2 1165.5 1098.0(Drosophila) 162 230529_PM_at HECA headcase homolog 7.08E−06 2.13E−03585.1 364.3 418.4 (Drosophila) 163 1562063_PM_x_at KIAA1245 /// KIAA1245/// 7.35E−06 2.20E−03 350.4 238.8 260.8 NBPF1 /// neuroblastoma NBPF10/// breakpoint family, NBPF11 /// member 1 /// NBPF12 /// neuroblastomaNBPF24 /// breakpoint fam NBPF8 /// NBPF9 164 202769_PM_at CCNG2 cyclinG2 7.42E−06 2.20E−03 697.1 1164.0 1264.6 165 1556493_PM_a_at KDM4Clysine (K)-specific 7.64E−06 2.24E−03 81.4 49.0 44.5 166 216509_PM_x_atMLLT10 demethylase 4C 7.64E−06 2.24E−03 22.4 17.9 19.3 myeloid/lymphoidor mixed-lineage leukemia (trithorax homolog, Drosophila); translocate167 223697_PM_x_at C9orf64 chromosome 9 open 7.70E−06 2.25E−03 1013.6771.2 836.8 reading frame 64 168 235999_PM_at — — 7.77E−06 2.25E−03227.6 174.1 182.1 169 244766_PM_at LOC100271836 /// SMG1 homolog,8.03E−06 2.31E−03 133.4 99.4 87.5 LOC440354 /// phosphatidylinositolLOC595101 /// 3-kinase-related LOC641298 /// kinase pseudogene /// SMG1PI-3-kinase-r 170 230332_PM_at ZCCHC7 Zinc finger, CCHC 8.07E−062.31E−03 467.4 265.1 263.2 domain containing 7 171 235308_PM_at ZBTB20zinc finger and BTB 8.17E−06 2.32E−03 256.7 184.2 167.3 domaincontaining 20 172 242492_PM_at CLNS1A Chloride channel, 8.19E−062.32E−03 128.5 82.8 79.2 nucleotide-sensitive, 1A 173 215898_PM_at TTLL5tubulin tyrosine 8.24E−06 2.32E−03 20.9 14.0 13.8 ligase-like family,member 5 174 244840_PM_x_at DOCK4 dedicator of 8.65E−06 2.42E−03 43.116.5 21.5 cytokinesis 4 175 220235_PM_s_at C1orf103 chromosome 1 open8.72E−06 2.43E−03 88.4 130.5 143.3 reading frame 103 176 229467_PM_atPCBP2 Poly(rC) binding 8.80E−06 2.44E−03 186.5 125.4 135.8 protein 2 177232527_PM_at — — 8.99E−06 2.48E−03 667.4 453.9 461.3 178 243286_PM_at —— 9.24E−06 2.53E−03 142.6 98.2 87.2 179 215628_PM_x_at — — 9.28E−062.53E−03 49.6 36.3 39.4 180 1556412_PM_at — — 9.45E−06 2.56E−03 34.924.7 23.8 181 204786_PM_s_at IFNAR2 interferon (alpha, 9.64E−06 2.59E−03795.6 573.0 639.2 beta and omega) receptor 2 182 234258_PM_at — —9.73E−06 2.60E−03 27.4 17.8 20.3 183 233274_PM_at — — 9.76E−06 2.60E−03109.9 77.5 79.4 184 239784_PM_at — — 9.82E−06 2.60E−03 137.0 80.1 70.1185 242498_PM_x_at — — 1.01E−05 2.65E−03 59.2 40.4 38.9 186 231351_PM_at— — 1.02E−05 2.67E−03 124.8 70.8 60.6 187 222368_PM_at — — 1.03E−052.67E−03 89.9 54.5 44.3 188 236524_PM_at — — 1.03E−05 2.67E−03 313.2234.7 214.2 189 243834_PM_at TNRC6A trinucleotide repeat 1.04E−052.67E−03 211.8 145.1 146.9 containing 6A 190 239167_PM_at — — 1.04E−052.67E−03 287.4 150.2 160.3 191 239238_PM_at — — 1.05E−05 2.67E−03 136.081.6 92.0 192 237194_PM_at — — 1.05E−05 2.67E−03 57.2 34.4 27.9 193242772_PM_x_at — — 1.06E−05 2.67E−03 299.2 185.2 189.4 194 243827_PM_at— — 1.06E−05 2.67E−03 115.9 50.1 56.4 195 1552536_PM_at VTI1A vesicletransport 1.10E−05 2.75E−03 61.7 35.1 34.6 through interaction witht-SNAREs homolog 1A (yeast) 196 243696_PM_at KIAA0562 KIAA0562 1.12E−052.77E−03 19.0 14.8 15.0 197 233648_PM_at — — 1.12E−05 2.77E−03 33.9 21.024.1 198 225858_PM_s_at XIAP X-linked inhibitor of 1.16E−05 2.85E−031020.7 760.3 772.6 apoptosis 199 238736_PM_at REV3L REV3-like, catalytic1.19E−05 2.91E−03 214.2 135.8 151.6 subunit of DNA polymerase zeta(yeast) 200 221192_PM_x_at MFSD11 major facilitator 1.20E−05 2.92E−03100.4 74.5 81.2 superfamily domain containing 11

TABLE 9 33 probesets that differentiate SCAR and TX at p-value <0.001 inPAXGene blood tubes FFold- Change Gene p-value (SCAR SCAR- TX- ProbesetID Symbol Gene Title (Phenotype) vs. TX) ID Mean Mean 1553094_PM_at TAC4tachykinin 4 0.000375027 −1.1 1553094_PM_at 8.7 9.6 (hemokinin)1553352_PM_x_at ERVWE1 endogenous retroviral 0.000494742 −1.261553352_PM_x_at 15.5 19.6 family W, env(C7), member 1 1553644_PM_atC14orf49 chromosome 14 open 0.000868817 −1.16 1553644_PM_at 10.1 11.7reading frame 49 1556178_PM_x_at TAF8 TAF8 RNA 0.000431074   1.241556178_PM_x_at 39.2 31.7 polymerase II, TATA box binding protein(TBP)-associated factor, 43 kDa 1559687_PM_at TMEM221 transmembrane8.09E−05 −1.16 1559687_PM_at 13.0 15.1 protein 221 1562492_PM_atLOC340090 hypothetical 0.00081096 −1.1 1562492_PM_at 8.8 9.7 LOC3400901563204_PM_at ZNF627 Zinc finger protein 0.000784254 −1.15 1563204_PM_at10.6 12.2 627 1570124_PM_at — — 0.000824814 −1.14 1570124_PM_at 10.612.2 204681_PM_s_at RAPGEF5 Rap guanine 0.000717727 −1.18 204681_PM_s_at9.6 11.3 nucleotide exchange factor (GEF) 5 206154_PM_at RLBP1retinaldehyde binding 0.000211941 −1.13 206154_PM_at 11.0 12.4 protein 1209053_PM_s_at WHSC1 Wolf-Hirschhorn 0.000772412   1.23 209053_PM_s_at15.1 12.3 syndrome candidate 1 209228_PM_x_at TUSC3 tumor suppressor0.000954529 −1.13 209228_PM_x_at 8.9 10.1 candidate 3 211701_PM_s_at TROtrophinin 0.000684486 −1.13 211701_PM_s_at 10.0 11.3 213369_PM_at CDHR1cadherin-related 0.000556648 −1.14 213369_PM_at 10.8 12.3 family member1 215110_PM_at MBL1P mannose-binding 0.000989176 −1.13 215110_PM_at 9.210.4 lectin (protein A) 1, pseudogene 215232_PM_at ARHGAP44 Rho GTPase0.000332776 −1.18 215232_PM_at 11.1 13.1 activating protein 44217158_PM_at LOC442421 hypothetical 2.98E−05   1.18 217158_PM_at 14.212.0 LOC442421 /// prostaglandin E2 receptor EP4 subtype- like218365_PM_s_at DARS2 aspartyl-tRNA 0.000716035   1.18 218365_PM_s_at17.2 14.5 synthetase 2, mitochondrial 219695_PM_at SMPD3 sphingomyelin0.000377151 −1.47 219695_PM_at 12.0 17.6 phosphodiesterase 3, neutralmembrane (neutral sphingomyelinase II) 220603_PM_s_at MCTP2 multiple C2domains, 0.000933412 −1.38 220603_PM_s_at 338.5 465.8 transmembrane 2224963_PM_at SLC26A2 solute carrier family 0.000961242   1.47224963_PM_at 94.3 64.0 26 (sulfate transporter), member 2 226729_PM_atUSP37 ubiquitin specific 0.000891038   1.24 226729_PM_at 32.9 26.6peptidase 37 228226_PM_s_at ZNF775 zinc finger protein 775 0.000589512  1.2 228226_PM_s_at 20.5 17.1 230608_PM_at C1orf182 chromosome 1 open0.000153478 −1.18 230608_PM_at 15.9 18.8 reading frame 182 230756_PM_atZNF683 zinc finger protein 683 0.00044751   1.52 230756_PM_at 26.7 17.6231757_PM_at TAS2R5 taste receptor, type 2, 0.000869775 −1.12231757_PM_at 9.3 10.4 member 5 231958_PM_at C3orf31 Chromosome 3 open4.09E−05   1.22 231958_PM_at 20.1 16.4 reading frame 31 237290_PM_at — —0.000948318 −1.22 237290_PM_at 10.3 12.5 237806_PM_s_at LOC729296hypothetical 0.00092234 −1.18 237806_PM_s_at 10.2 12.0 LOC729296238459_PM_x_at SPATA6 spermatogenesis 0.000116525 −1.15 238459_PM_x_at9.2 10.5 associated 6 241331_PM_at SKAP2 Src kinase associated0.000821476 −1.39 241331_PM_at 16.4 22.9 phosphoprotein 2 241368_PM_atPLIN5 perilipin 5 0.000406066 −1.61 241368_PM_at 84.5 136.3 241543_PM_at— — 0.000478221 −1.17 241543_PM_at 9.4 11.0

Example 3

Differentially Expressed Genes Associated with Kidney TransplantRejections

This Example describes global analysis of gene expressions in kidneytransplant patients with different types of rejections or injuries.

A total of biopsy-documented 274 kidney biopsy samples from theTransplant Genomics Collaborative Group (TGCG) were processed on theAffymetrix HG-U133 PM only peg microarrays. The 274 samples that wereanalyzed comprised of 4 different phenotypes: Acute Rejection (AR;n=75); Acute Dysfunction No Rejection (ADNR; n=39); Chronic AllograftNephropathy (CAN; n=61); and Transplant Excellent (TX; n=99).

Signal Filters: To eliminate low expressed signals we used a signalfilter cut-off that was data driven, and expression signals <Log 2 4.23in all samples were eliminated leaving us with 48882 probe sets from atotal of 54721 probe sets.

4-Way AR/ADNR/CAN/TX Classifier:

We first did a 4 way comparison of the AR, ADNR, CAN and TX samples. Thesamples comprised of four different classes a 4-way ANOVA analysisyielded more than 10,000 differentially expressed genes even at astringent p value cut-off of <0.001. Since we were trying to discover asignature that could differentiate these four classes we used only thetop 200 differentially expressed probe sets to build predictive models.We ran the Nearest Centroid (NC) algorithm to build the predictivemodels. When we used the top 200 differentially expressed probe setsbetween all four phenotypes, the best predictor model was based on 199probe sets.

Nearest Centroid (NC) classification takes the gene expression profileof a new sample, and compares it to each of the existing classcentroids. The class whose centroid that it is closest to, in squareddistance, is the predicted class for that new sample. It also providesthe centroid distances for each sample to each of the possiblephenotypes being tested. In other words, in a 2-way classifier like ARvs. TX, the tool provides the “best” classification and provides thecentroid distances to the two possible outcomes: TX and AR.

We observed in multiple datasets that there are 4 classes of predictionsmade. First, are correctly classified as TX by both biopsy and NC.Second, are correctly classified as AR by both biopsy and NC. Third, aretruly misclassified samples. In other words, the biopsy says one thingand the molecular profile another. In these cases, the centroiddistances for the given classifications are dramatically different,making the molecular classification very straightforward and simply notconsistent with the biopsy phenotype assigned. Whether this is becausethe gold standard biopsy classification is wrong or the molecularclassification is wrong is impossible to know at this point.

However, there is a fourth class that we call “mixed” classifications.In these cases supposedly “misclassified” samples by molecular profileshow a nearest centroid distance that is not very different whencompared to that of the “correct” classification based on the biopsy. Inother words, the nearest centroid distances of most of thesemisclassified “mixed” samples are actually very close to the correctbiopsy classification. However, because NC has no rules set to deal withthe mixed situation it simply calls the sample by the nominally highercentroid distance.

The fact is that most standard implementations of class predictionalgorithms currently available treat all classes as dichotomousvariables (yes/no diagnostically). They are not designed to deal withthe reality of medicine that molecular phenotypes of clinical samplescan actually represent a continuous range of molecular scores based onthe expression signal intensities with complex implications for thediagnoses. Thus, “mixed” cases where the centroid distances are onlyslightly higher for TX than AR is still classified as a TX, even if theAR distances are only slightly less. In this case, where there is amixture of TX and AR by expression, it is obvious that the case isactually an AR for a transplant clinician, not a TX. Perhaps just amilder form of AR and this is the reason for using thresholding.

Thus, we set a threshold for the centroid distances. The threshold isdriven by the data. The threshold equals the mean difference NC providesin centroid distances for the two possible classifications (i.e. AR vs.TX) for all correctly classified samples in the data set (e.g. classes 1and 2 of the 4 possible outcomes of classification). This means that forthe “mixed” class of samples, if a biopsy-documented sample wasmisclassified by molecular profiling, but the misclassification waswithin the range of the mean calculated centroid distances of the trueclassifications in the rest of the data, then that sample would not beconsidered as a misclassified sample.

Table 10a shows the performance of the 4 way AR, ADNR, CAN, TX NCclassifier using such a data driven threshold. Table 10b shows the top200 probeset used for the 4 way AR, ADNR, CAN, TX NC classifier. So,using the top 200 differentially expressed probesets from a 4-way AR,ADNR, CAN and TX ANOVA with a Nearest Centroid classifier, we are ableto molecularly classify the 4 phenotypes at 97% accuracy. Smallerclassifier sets did not afford any significant increase in thepredictive accuracies. To validate this data we applied thisclassification to an externally collected data set. These were samplescollected at the University of Sao Paolo in Brazil. A total of 80biopsy-documented kidney biopsy samples were processed on the sameAffymetrix HG-U133 PM only peg microarrays. These 80 samples that wereanalyzed comprised of the same 4 different phenotypes: AR (n=23); ADNR(n=11); CAN (n=29); and TX (n=17).

We performed the classification based on the “locked” NC predictor(meaning that none of the thresholding parameters were changed. Table 11shows the performance of our locked 4 way AR, ADNR, CAN, TX NCclassifier in the Brazilian cohort. So, using the top 200 differentiallyexpressed probesets from a 4-way AR, ADNR, CAN and TX ANOVA with a“locked” Nearest Centroid classifier we are able to molecularly classifythe 4 phenotypes with similar accuracy in an independently andexternally collected validation set. This validates our molecularclassifier of the biopsy on an independent external data set. It alsodemonstrates that the classifier is not subject to influence based onsignificant racial differences represented in the Brazilian population.

3-Way AR/ADNR/TX Classifier:

Similarly, we did a 3 way comparison of the AR, ADNR and TX samplessince these are the most common phenotypes encountered during the earlypost-transplant period with CAN usually being a late manifestation ofgraft injury which is progressive. The samples comprised of these 3different classes, and a 4-way ANOVA analysis again yielded more than10,000 differentially expressed genes, so we used only the top 200differentially expressed probe sets to build predictive models. We ranthe Nearest Centroid (NC) algorithm to build the predictive models. Whenwe used the top 200 differentially expressed probe sets between all fourphenotypes the best predictor model was based on 197 probe sets.

Table 12a shows the performance of the 3 way AR, ADNR, TX NC classifierwith which we are able to molecularly classify the 3 phenotypes at 98%accuracy in the TGCG cohort. Table 12b shows the top 200 probeset usedfor the 3 way AR, ADNR, TX NC classifier in the TGCG cohort. Similarlythe locked 3 way classifier performs equally well on the Braziliancohort with 98% accuracy (Table 13). Therefore, our 3 way classifieralso validates on the external data set.

2-Way CAN/TX Classifier:

Finally we also did a 2 way comparison of the CAN and TX samples. Thesamples comprised of these 2 classes with an ANOVA analysis againyielded ˜11,000 differentially expressed genes, so we used only the top200 differentially expressed probe sets to build predictive models. Weran the Nearest Centroid (NC) algorithm to build the predictive models.When we used the top 200 differentially expressed probe sets the bestpredictor model was based on all 200 probe sets. Table 14a shows theperformance of the 2 way CAN, TX NC classifier with which we are able tomolecularly classify the 4 phenotypes at 97% accuracy in the TGCGcohort. Table 14b shows the top 200 probeset used for the 2 way CAN, TXNC classifier in the TGCG cohort. This locked classifier performsequally well on the Brazilian cohort with 95% accuracy (Table 15). Againwe show that our 2 way CAN, TX classifier also validates on the externaldata set.

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. Although any methods and materials similaror equivalent to those described herein can be used in the practice ortesting of the present invention, the preferred methods and materialsare described.

All publications, GenBank sequences, ATCC deposits, patents and patentapplications cited herein are hereby expressly incorporated by referencein their entirety and for all purposes as if each is individually sodenoted.

TABLE 10a Biopsy Expression Profiling of Kidney Transplants: 4-WayClassifier AR vs. ADNR vs. CAN vs. TX (TGCG Samples) Validation CohortPostive Negative Predictive Predictive Predictive Accuracy SensitivitySpecificity Value Value Algorithm Predictors Comparison AUC (%) (%) (%)(%) (%) Nearest Centroid 199 AR vs. TX 0.957 95 96 96 94 97 NearestCentroid 199 ADNR vs. TX 0.977 97 94 100 100 97 Nearest Centroid 199 CANvs. TX 0.992 99 98 100 100 99

TABLE 10b Biopsy Expression Profiling of Kidney Transplants: 4-WayClassifier AR vs. ADNR vs. CAN vs. TX (TGCG Samples) p-value ProbesetEntrez Gene (Final ADNR- AR- CAN- TX- # ID Gene Symbol Gene TitlePhenotype) Mean Mean Mean Mean 1 204446_PM_s_at 240 ALOX5 arachidonate5-lipoxygenase 2.82E−34 91.9 323.9 216.7 54.7 2 202207_PM_at 10123 ARL4CADP-ribosylation factor- 1.31E−32 106.9 258.6 190.4 57.2 like 4C 3204698_PM_at 3669 ISG20 interferon stimulated 1.50E−31 41.5 165.1 96.127.6 exonuclease gene 20 kDa 4 225701_PM_at 80709 AKNA AT-hooktranscription 1.75E−31 37.7 102.8 73.2 29.0 factor 5 207651_PM_at 29909GPR171 G protein-coupled receptor 6.30E−31 25.8 89.9 57.0 20.9 171 6204205_PM_at 60489 APOBE apolipoprotein B mRNA 1.27E−30 95.4 289.4 192.078.7 C3G editing enzyme, catalytic polypeptide-like 3G 7 208948_PM_s_at6780 STAU1 staufen, RNA binding 1.37E−30 1807.9 1531.8 1766.0 2467.4protein, homolog 1 (Drosophila) 8 217733_PM_s_at 9168 TMSB10 thymosinbeta 10 2.38E−30 4414.7 6331.3 5555.2 3529.0 9 205831_PM_at 914 CD2 CD2molecule 2.73E−30 40.4 162.5 100.9 33.9 10 209083_PM_at 11151 CORO1Acoronin, actin binding 5.57E−30 46.9 163.8 107.1 34.3 protein, 1A 11210915_PM_x_at 28638 TRBC2 T cell receptor beta constant 2 5.60E−30 39.7230.7 129.7 37.5 12 211368_PM_s_at 834 CASP1 caspase 1,apoptosis-related 6.21E−30 102.6 274.3 191.4 81.8 cysteine peptidase(interleukin 1, beta, convertase) 13 201042_PM_at 7052 TGM2transglutaminase 2 (C 6.28E−30 131.8 236.5 172.6 80.1 polypeptide,protein- glutamine-gamma- glutamyltransferase) 14 227353_PM_at 147138TMC8 transmembrane channel-like 8 7.76E−30 19.8 64.2 42.7 16.6 151555852_PM_at 100507463 LOC100507463 hypothetical 8.29E−30 78.9 202.6154.2 70.9 LOC100507463 16 226878_PM_at 3111 HLA-DOA majorhistocompatibility 1.63E−29 102.0 288.9 201.4 94.3 complex, class II, DOalpha 17 238327_PM_at 440836 ODF3B outer dense fiber of sperm 1.74E−2932.8 81.4 58.5 26.1 tails 3B 18 229437_PM_at 114614 MIR155HG MIR155 hostgene (non- 1.78E−29 15.4 50.4 28.5 12.9 protein coding) 19 33304_PM_at3669 ISG20 interferon stimulated 2.40E−29 33.2 101.3 63.4 22.1exonuclease gene 20 kDa 20 226621_PM_at 9180 OSMR oncostatin M receptor2.42E−29 545.6 804.5 682.9 312.1 21 1553906_PM_s_at 221472 FGD2 FYVE,RhoGEF and PH 2.43E−29 104.6 321.0 219.3 71.9 domain containing 2 221405_PM_i_at 6352 CCL5 chemokine (C—C motif) 2.54E−29 68.0 295.7 195.654.6 ligand 5 23 226219_PM_at 257106 ARHG Rho GTPase activating 2.92E−2946.4 127.9 91.8 37.5 AP30 protein 30 24 204891_PM_s_at 3932 LCKlymphocyte-specific protein 3.79E−29 19.3 74.2 43.4 17.8 tyrosine kinase25 210538_PM_s_at 330 BIRC3 baculoviral IAP repeat- 5.06E−29 106.7 276.7199.1 84.6 containing 3 26 202644_PM_s_at 7128 TNFAI tumor necrosisfactor, alpha- 5.47E−29 169.8 380.4 278.2 136.6 P3 induced protein 3 27227346_PM_at 10320 IKZF1 IKAROS family zinc finger 1 7.07E−29 24.9 79.753.3 19.8 (Ikaros) 28 202957_PM_at 3059 HCLS1 hematopoieticcell-specific 8.26E−29 119.2 299.5 229.9 82.2 Lyn substrate 1 29202307_PM_s_at 6890 TAP1 transporter 1, ATP-binding 1.01E−28 172.4 420.6280.0 141.0 cassette, sub-family B (MDR/TAP) 30 202748_PM_at 2634 GBP2guanylate binding protein 2, 1.10E−28 196.7 473.0 306.7 141.0interferon-inducible 31 211796_PM_s_at 28638 /// TRBC1 /// T cellreceptor beta constant 1.31E−28 69.2 431.5 250.2 63.6 28639 TRBC2 1 ///T cell receptor beta constant 2 32 213160_PM_at 1794 DOCK2 dedicator ofcytokinesis 2 1.36E−28 33.7 92.6 66.0 27.8 33 211656_PM_x_at 100133583/// HLA-DQB1 /// major histocompatibility 1.63E−28 211.3 630.2 459.8208.2 3119 LOC100133583 complex, class II, DQ beta 1 /// HLA class IIhistocompatibili 34 223322_PM_at 83593 RASSF5 Ras association 1.68E−2841.5 114.4 79.3 39.2 (RalGDS/AF-6) domain family member 5 35205488_PM_at 3001 GZMA granzyme A (granzyme 1, 1.72E−28 37.3 164.8 102.333.4 cytotoxic T-lymphocyte- associated serine esterase 3) 36213603_PM_s_at 5880 RAC2 ras-related C3 botulinum 1.87E−28 113.9 366.5250.3 86.5 toxin substrate 2 (rho family, small GTP binding proteinRac2) 37 229390_PM_at 441168 FAM26F family with sequence 1.94E−28 103.8520.0 272.4 75.9 38 206804_PM_at 917 CD3G CD3g molecule, gamma 1.99E−2819.7 60.6 36.4 17.3 (CD3-TCR complex) 39 209795_PM_at 969 CD69 CD69molecule 2.06E−28 17.6 57.6 40.6 15.2 40 219574_PM_at 55016 1-Marmembrane-associated ring 2.07E−28 51.5 126.0 87.5 36.2 finger (C3HC4) 141 207320_PM_x_at 6780 STAU1 staufen, RNA binding 2.21E−28 1425.1 1194.61383.2 1945.3 protein, homolog 1 (Drosophila) 42 218983_PM_at 51279 C1RLcomplement component 1, r 2.97E−28 167.1 244.5 206.9 99.4subcomponent-like 43 206011_PM_at 834 CASP1 caspase 1, apoptosis-related3.23E−28 74.5 198.0 146.2 60.7 cysteine peptidase (interleukin 1, beta,convertase) 44 213539_PM_at 915 CD3D CD3d molecule, delta 5.42E−28 70.8335.1 168.1 60.0 (CD3-TCR complex) 45 213193_PM_x_at 28639 TRBC1 T cellreceptor beta constant 1 5.69E−28 95.3 490.9 286.5 92.1 46232543_PM_x_at 64333 ARHGAP9 Rho GTPase activating 6.79E−28 31.7 99.162.3 25.8 protein 9 47 200986_PM_at 710 SERPING1 serpin peptidaseinhibitor, 7.42E−28 442.7 731.5 590.2 305.3 clade G (Cl inhibitor),member 1 48 213037_PM_x_at 6780 STAU1 staufen, RNA binding 9.35E−281699.0 1466.8 1670.1 2264.9 protein, homolog 1 (Drosophila) 49204670_PM_x_at 3123 /// 3126 HLA-DRB1 /// major histocompatibility1.03E−27 2461. 4344. 3694. 2262. HLA-DRB4 complex, class II, DR beta 1/// major histocompatibility comp 50 217028_PM_at 7852 CXCR4 chemokine(C—X—C motif) 1.52E−27 100.8 304.4 208.4 75.3 receptor 4 51 203761_PM_at6503 SLA Src-like-adaptor 1.61E−27 69.6 179.2 138.4 51.4 52201137_PM_s_at 3115 HLA-DPB1 major histocompatibility 1.95E−27 1579.13863.7 3151.7 1475.9 complex, class II, DP beta 1 53 205269_PM_at 3937LCP2 lymphocyte cytosolic 2.16E−27 30.2 92.2 58.9 22.9 protein 2 (SH2domain containing leukocyte protein of 76 kDa) 54 205821_PM_at 22914KLRK1 killer cell lectin-like 2.56E−27 30.3 111.5 72.5 31.3 receptorsubfamily K, member 1 55 204655_PM_at 6352 CCL5 chemokine (C—C motif)3.28E−27 77.5 339.4 223.4 66.8 ligand 5 56 226474_PM_at 84166 NLRC5 NLRfamily, CARD domain 3.54E−27 64.4 173.5 129.8 55.1 containing 5 57212503_PM_s_at 22982 DIP2C DIP2 disco-interacting 3.69E−27 559.7 389.2502.0 755.0 protein 2 homolog C (Drosophila) 58 213857_PM_s_at 961 CD47CD47 molecule 4.33E−27 589.6 858.0 703.2 481.2 59 206118_PM_at 6775STAT4 signal transducer and 4.58E−27 21.0 49.5 37.7 18.1 activator oftranscription 4 60 227344_PM_at 10320 IKZF1 IKAROS family zinc finger 15.87E−27 17.8 40.0 28.5 14.9 (Ikaros) 61 230550_PM_at 64231 MS4A6Amembrane-spanning 4- 5.98E−27 44.8 124.3 88.0 30.9 domains, subfamily A,member 6A 62 235529_PM_x_at 25939 SAMHD1 SAM domain and HID 6.56E−27189.3 379.9 289.1 128.0 domain 1 63 205758_PM_at 925 CD8A CD8a molecule7.28E−27 24.2 105.8 60.3 22.2 64 211366_PM_x_at 834 CASP1 caspase 1,apoptosis-related 7.37E−27 115.3 261.0 186.0 87.1 cysteine peptidase(interleukin 1, beta, convertase) 65 209606_PM_at 9595 CYTIP cytohesin 1interacting 7.48E−27 41.4 114.3 79.0 32.9 protein 66 201721_PM_s_at 7805LAPTM5 lysosomal protein 8.04E−27 396.5 934.6 661.3 249.4 transmembrane5 67 204774_PM_at 2123 EVI2A ecotropic viral integration 8.14E−27 63.6168.5 114.7 44.9 site 2A 68 215005_PM_at 54550 NECAB2 N-terminal EF-handcalcium 8.32E−27 36.7 23.4 30.9 65.7 binding protein 2 69 229937_PM_x_at10859 LILRB1 Leukocyte immunoglobulin- 8.33E−27 23.5 79.9 50.0 18.5 likereceptor, subfamily B (with TM and ITIM domains), member 70209515_PM_s_at 5873 RAB27A RAB27A, member RAS 8.93E−27 127.3 192.2 160.585.2 oncogene family 71 242916_PM_at 11064 CEP110 centrosomal protein110 kDa 8.98E−27 30.8 68.1 51.2 26.2 72 205270_PM_s_at 3937 LCP2lymphocyte cytosolic 9.04E−27 56.8 162.6 104.4 44.6 protein 2 (SH2domain containing leukocyte protein of 76 kDa) 73 214022_PM_s_at 8519IFITM1 interferon induced 9.31E−27 799.1 1514.7 1236.6 683.3transmembrane protein 1 (9-27) 74 1552703_PM_s_at 114769 /// 834 CARD16/// caspase recruitment domain 1.01E−26 64.6 167.8 120.6 54.9 CASP1family, member 16 /// caspase 1, apoptosis-related cysteine 75202720_PM_at 26136 TES testis derived transcript 1.05E−26 285.4 379.0357.9 204.2 (3 LIM doma ins) 76 202659_PM_at 5699 PSMB10 proteasome(prosome, 1.10E−26 180.7 355.6 250.3 151.5 macropain) subunit, betatype, 10 77 236295_PM_s_at 197358 NLRC3 NLR family, CARD domain 1.19E−2619.0 52.5 37.0 18.6 containing 3 78 229041_PM_s_at — — — 1.31E−26 36.5132.1 84.7 32.4 79 205798_PM_at 3575 IL7R interleukin 7 receptor1.32E−26 44.1 136.8 106.3 33.4 80 209970_PM_x_at 834 CASP1 caspase 1,apoptosis-related 1.36E−26 116.0 266.6 181.6 88.7 cysteine peptidase(interleukin 1, beta, convertase) 81 204336_PM_s_at 10287 RGS19regulator of G-protein 1.54E−26 95.2 187.7 135.7 67.0 signaling 19 82204912_PM_at 3587 IL10RA interleukin 10 receptor, 1.61E−26 57.0 178.7117.2 46.1 alpha 83 227184_PM_at 5724 PTAFR platelet-activating factor1.70E−26 89.8 191.4 134.4 62.7 receptor 84 209969_PM_s_at 6772 STAT1signal transducer and 1.82E−26 395.8 1114.5 664.6 320.8 activator oftranscription 1, 91 kDa 85 232617_PM_at 1520 CTSS cathepsin S 1.88E−26209.6 537.9 392.2 154.8 86 224451_PM_x_at 64333 ARHGAP9 Rho GTPaseactivating 1.94E−26 34.2 103.4 71.8 29.4 protein 9 87 209670_PM_at 28755T cell receptor alpha 2.06E−26 37.9 149.9 96.2 38.5 TRAC constant 881559584_PM_a_at 283897 C16orf54 chromosome 16 open 2.22E−26 31.3 95.871.5 26.1 reading frame 54 89 208306_PM_x_at 3123 HLA-DRB1 Majorhistocompatibility 2.29E−26 2417.5 4278.5 3695.8 2255.0 complex, classII, DR beta 1 90 229383_PM_at 55016 1-Mar membrane-associated ring2.36E−26 33.8 88.0 52.1 22.9 finger (C3HC4) 1 91 235735_PM_at — — —2.46E−26 13.0 34.9 24.5 11.2 92 203416_PM_at 963 CD53 CD53 molecule2.56E−26 215.9 603.0 422.7 157.8 93 212504_PM_at 22982 DIP2C DIP2disco-interacting 3.21E−26 334.5 227.2 289.4 452.5 protein 2 homolog C(Drosophila) 94 204279_PM_at 5698 PSMB9 proteasome (prosome, 3.45E−26241.6 637.4 419.4 211.3 macropain) subunit, beta type, 9 (largemultifunctional peptidase 95 235964_PM_x_at 25939 SAMHD1 SAM domain andHID 3.60E−26 172.9 345.9 270.1 117.5 domain 1 96 213566_PM_at 6039RNASE6 ribonuclease, RNase A 3.84E−26 180.9 482.0 341.1 134.3 family, k697 221698_PM_s_at 64581 CLEC7A C-type lectin domain family 4.00E−26 61.5164.6 112.8 49.7 7, member A 98 227125_PM_at 3455 IFNAR2 interferon(alpha, beta and 4.03E−26 70.0 126.2 96.7 55.8 omega) receptor 2 99226525_PM_at 9262 STK17B serine/threonine kinase 17b 4.14E−26 146.8338.7 259.1 107.6 100 221666_PM_s_at 29108 PYCARD PYD and CARD domain4.95E−26 60.7 132.8 95.5 44.7 containing 101 209774_PM_x_at 2920 CXCL2chemokine (C—X—C motif) 5.73E−26 24.9 52.9 38.2 15.5 ligand 2 102206082_PM_at 10866 HCP5 HLA complex P5 5.98E−26 76.2 185.1 129.0 66.6103 229391_PM_s_at 441168 FAM26F family with sequence 6.03E−26 98.6379.7 212.1 73.7 similarity 26, member F 104 229295_PM_at 150166 ///23765 IL17RA /// interleukin 17 receptor A /// 6.13E−26 76.4 131.8 98.450.0 LOC150166 hypothetical protein LOC150166 105 202901_PM_x_at 1520CTSS cathepsin S 6.32E−26 67.8 180.5 130.9 45.1 106 226991_PM_at 4773NFATC2 nuclear factor of activated 6.49E−26 37.9 87.0 66.7 30.3 T-cells,cytoplasmic, calcineurin-dependent 2 107 223280_PM_x_at 64231 M54A6Amembrane-spanning 4- 6.72E−26 269.0 711.8 451.2 199.5 domans, subfamilyA, member 6A 108 201601_PM_x_at 8519 IFITM1 interferon induced 7.27E−261471.3 2543.5 2202.5 1251.1 transmembrane protein 1 (9-27) 1091552701_PM_a_at 114769 CARD16 caspase recruitment domain 7.33E−26 143.8413.6 273.3 119.8 family, member 16 110 229625_PM_at 115362 GBPSguanylate binding protein 5 7.80E−26 29.3 133.3 68.6 24.0 11138149_PM_at 9938 ARHGAP25 Rho GTPase activating 9.83E−26 51.3 108.9 83.243.2 protein 25 112 203932_PM_at 3109 HLA- major histocompatibility1.03E−25 422.4 853.9 633.5 376.0 DMB complex, class II, DM beta 113228964_PM_at 639 PRDM1 PR domain containing 1, 1.15E−25 21.2 52.1 41.517.0 with ZNF domain 114 225799_PM_at 112597 /// LOC541471 ///hypothetical LOC541471 /// 1.23E−25 230.5 444.3 339.2 172.0 541471NCRNA00152 non-protein coding RNA 152 115 204118_PM_at 962 CD48 CD48molecule 1.34E−25 82.9 341.5 212.4 65.2 116 211742_PM_s_at 2124 EVI2Becotropic viral integration 1.36E−25 73.9 236.5 166.2 53.2 site 2B 117213416_PM_at 3676 ITGA4 integrin, alpha 4 (antigen 1.47E−25 26.3 78.150.8 22.8 CD49D, alpha 4 subunit of VLA-4 receptor) 118 211991_PM_s_at3113 HLA-DPA1 major histocompatibility 1.50E−25 1455.0 3605.4 2837.21462.9 complex, class II, DP alpha 1 119 232024_PM_at 26157 GIMAP2GTPase, IMAP family 1.57E−25 90.2 197.7 146.7 72.5 member 2 120205159_PM_at 1439 CSF2RB colony stimulating factor 2 1.73E−25 33.7 107.570.4 26.3 receptor, beta, low-affinity (granulocyte-macrophage) 121228471_PM_at 91526 ANKRD44 ankyrin repeat domain 44 1.79E−25 106.1 230.3184.6 86.5 122 203332_PM_s_at 3635 INPP5D inositol polyphosphate-5-1.88E−25 27.9 60.5 42.6 24.0 phosphatase, 145 kDa 123 223502_PM_s_at10673 TNFSF13B tumor necrosis factor 2.02E−25 73.0 244.3 145.5 60.0(ligand) superfamily, member 13b 124 229723_PM_at 117289 TAGAP T-cellactivation 2.07E−25 29.2 82.9 55.9 26.2 RhoGTPase activating protein 125206978_PM_at 729230 CCR2 chemokine (C—C motif) 2.17E−25 32.1 100.7 68.627.3 receptor 2 126 1555832_PM_s_at 1316 KLF6 Kruppel-like factor 62.31E−25 899.4 1076.8 1003.3 575.1 127 211990_PM_at 3113 HLA-DPA1 majorhistocompatibility 2.53E−25 2990.7 5949.1 5139.9 3176.3 complex, classII, DP alpha 128 202018_PM_s_at 4057 LTF lactotransferrin 2.90E−25 392.31332.4 624.5 117.7 129 210644_PM_s_at 3903 LAIR1 leukocyte-associated2.90E−25 29.7 74.6 45.3 21.2 immunoglobulin-like receptor 1 130222294_PM_s_at 5873 RAB27A RAB27A, member RAS 3.13E−25 198.7 309.1 263.0146.4 oncogene family 131 238668_PM_at — — — 3.29E−25 18.2 49.2 33.614.5 132 213975_PM_s_at 4069 LYZ lysozyme 3.31E−25 458.4 1626.0 1089.7338.1 133 204220_PM_at 9535 GMFG glia maturation factor, 3.46E−25 147.0339.3 241.4 128.9 gamma 134 243366_PM_s_at — — — 3.46E−25 24.7 72.1 52.522.0 135 221932_PM_s_at 51218 GLRX5 glutaredoxin 5 3.64E−25 1351.51145.8 1218.1 1599.3 136 225415_PM_at 151636 DTX3L deltex 3-like(Drosophila) 3.77E−25 230.2 376.4 290.8 166.9 137 205466_PM_s_at 9957HS3ST1 heparan sulfate 4.15E−25 73.6 123.8 96.0 42.1 (glucosamine) 3-O—sulfotransferase 1 138 200904_PM_at 3133 HLA-E major histocompatibility4.20E−25 1142.5 1795.2 1607.7 994.7 complex, class I, E 139 228442_PM_at4773 NFATC2 nuclear factor of activated 4.48E−25 39.0 84.8 62.8 32.0T-cells, cytoplasmic, calcineurin-dependent 2 140 204923_PM_at 54440SASH3 SAM and SH3 domain 4.49E−25 25.4 68.2 47.6 21.7 containing 3 141223640_PM_at 10870 HCST hematopoietic cell signal 4.52E−25 91.0 234.0158.3 72.7 transducer 142 211582_PM_x_at 7940 LST1 leukocyte specifictranscript 1 4.53E−25 57.5 183.8 121.2 49.4 143 219014_PM_at 51316 PLAC8placenta-specific 8 5.94E−25 38.8 164.1 88.6 30.7 144 210895_PM_s_at 942CD86 CD86 molecule 6.21E−25 32.3 85.0 52.6 21.6 145 AFFX- 6772 STAT1signal transducer and 6.81E−25 642.1 1295.1 907.8 539.6 HUMISGF3A/activator of transcription 1, M97935_3_at 91 kDa 146 201315_PM_x_at10581 IFITM2 interferon induced 6.87E−25 2690.9 3712.1 3303.7 2175.3transmembrane protein 2 (1-8D) 147 228532_PM_at 128346 C1orf162chromosome 1 open reading 7.07E−25 82.6 217.7 140.2 60.0 frame 162 148202376_PM_at 12 SERPINA3 serpin peptidase inhibitor, 7.13E−25 186.2387.1 210.2 51.7 clade A (alpha-1 antiproteinase, antitrypsin), member 3149 212587_PM_s_at 5788 PTPRC protein tyrosine 7.18E−25 114.8 398.6265.7 90.3 phosphatase, receptor type, C 150 223218_PM_s_at 64332 NFKBIZnuclear factor of kappa light 7.26E−25 222.6 497.9 399.9 159.1polypeptide gene enhancer in B-cells inhibitor, zeta 151 224356_PM_x_at64231 MS4A6A membrane-spanning 4- 7.33E−25 150.6 399.6 249.6 111.2domains, subfamily A, member 6A 152 206420_PM_at 10261 IGSF6immunoglobulin 7.58E−25 45.1 131.5 74.3 32.7 superfamily, member 6 153225764_PM_at 2120 ETV6 ets variant 6 7.66E−25 92.6 133.0 112.8 77.0 1541555756_PM_a_at_ 64581 CLEC7A C-type lectin domain family 7.74E−25 16.645.4 28.9 13.2 7, member A 155 226218_PM_at 3575 IL7R interleukin 7receptor 8.14E−25 55.6 197.0 147.1 41.4 156 209198_PM_s_at 23208 SYT11synaptotagmin XI 8.28E−25 30.0 45.3 41.8 22.8 157 202803_PM_s_at 3689ITGB2 integrin, beta 2 9.57E−25 100.5 253.0 182.6 65.2 (complementcomponent 3 receptor 3 and 4 subunit) 158 215049_PM_x_at 9332 CD163CD163 molecule 9.85E−25 232.8 481.3 344.5 112.9 159 202953_PM_at 713C1QB complement component 1, q 9.99E−25 215.8 638.4 401.1 142.5subcomponent, B chain 160 208091_PM_s_at 81552 VOPP1 vesicular,overexpressed in 1.02E−24 495.5 713.9 578.1 409.7 cancer, prosurvivalprotein 1 161 201288_PM_at 397 ARHGDIB Rho GDP dissociation 1.13E−24354.9 686.8 542.2 308.1 inhibitor (GDI) beta 162 213733_PM_at 4542 MYO1Fmyosin IF 1.27E−24 26.8 52.7 39.4 20.9 163 212588_PM_at 5788 PTPRCprotein tyrosine 1.41E−24 94.4 321.0 217.7 76.4 phosphatase, receptortype, C 164 242907_PM_at — — — 1.49E−24 59.3 165.1 99.7 39.8 165209619_PM_at 972 CD74 CD74 molecule, major 1.55E−24 989.0 1864.7 1502.3864.9 histocompatibility complex, class II invariant chain 166239237_PM_at — — — 1.75E−24 15.9 34.9 25.3 14.5 167 217022_PM_s_at100126583 /// IGHA1 /// immunoglobulin heavy 1.80E−24 77.7 592.5 494.649.4 3493 /// 3494 IGHA2 /// constant alpha 1 /// LOC100126583immunoglobulin heavy constant alpha 2 (A2m ma 168 201859_PM_at 5552 SRGNserglycin 1.82E−24 1237.9 2171.99 1747.0 981.8 169 243418_PM_at — — —1.88E−24 56.3 31.1 49.8 104.8 170 202531_PM_at 3659 IRF1 interferonregulatory factor 1 1.93E−24 92.9 226.0 154.5 77.0 171 208966_PM_x_at3428 IFI16 interferon, gamma-inducible 1.98E−24 406.7 760.4 644.9 312.6protein 16 172 1555759_PM_a_at 6352 CCL5 chemokine (C—C motif) 2.02E−2481.4 350.8 233.2 68.3 ligand 5 173 202643_PM_s_at 7128 TNFAIP3 tumornecrosis factor, alpha- 2.11E−24 43.7 92.8 68.1 34.8 induced protein 3174 223922_PM_x_at 64231 MS4A6A membrane-spanning 4- 2.22E−24 289.2656.8 424.1 214.5 domains, subfamily A, member 6A 175 209374_PM_s_at3507 IGHM immunoglobulin heavy 2.26E−24 61.8 437.0 301.0 45.0 constantmu 176 227677_PM_at 3718 JAK3 Janus kinase 3 2.29E−24 18.6 51.7 32.015.5 177 221840_PM_at 5791 PTPRE protein tyrosine 2.38E−24 71.0 133.2102.5 51.8 phosphatase, receptor type, E 178 200887_PM_s_at 6772 STAT1signal transducer and 2.47E−24 1141.7 2278.6 1602.9 972.9 activator oftranscription 1, 91 kDa 179 221875_PM_x_at 3134 HLA-F majorhistocompatibility 2.72E−24 1365.3 2400.6 1971.8 1213.0 complex, classI, F 180 206513_PM_at 9447 AIM2 absent in melanoma 2 2.87E−24 17.2 50.730.5 13.9 181 214574_PM_x_at 7940 LST1 leukocyte specific transcript 12.95E−24 74.1 222.8 142.0 61.7 182 231776_PM_at 8320 EOMES eomesodermin3.07E−24 24.0 63.9 43.5 22.4 183 205639_PM_at 313 AOAH acyloxyacylhydrolase 4.03E−24 30.3 72.6 45.3 25.2 (neutrophil) 184 201762_PM_s_at5721 PSME2 proteasome (prosome, 4.45E−24 1251.6 1825.1 1423.4 1091.0macropain) activator subunit 2 (PA28 beta) 185 217986_PM_s_at 11177BAZ1A bromodom ainadjacent to 4.79E−24 87.5 145.2 116.4 62.5 zinc fingerdomain, 1A 186 235229_PM_at — — — 4.84E−24 50.9 210.6 135.0 41.9 187204924_PM_at 7097 TLR2 toll-like receptor 2 4.84E−24 96.8 162.0 116.666.6 188 202208_PM_s_at 10123 ARL4C ADP-ribosylation factor- 4.89E−2454.0 99.6 77.0 42.2 like 4C 189 227072_PM_at 25914 RTTN rotatin 5.01E−24101.1 74.5 83.6 132.9 190 202206_PM_at 10123 ARL4C ADP-ribosylationfactor- 5.08E−24 60.8 128.5 96.1 36.0 like 4C 191 204563_PM_at 6402 SELLselectin L 5.11E−24 40.7 134.7 76.1 31.7 192 219386_PM_s_at 56833 SLAMF8SLAM family member 8 5.17E−24 28.2 92.1 52.2 19.4 193 218232_PM_at 712C1QA complement component 1, q 5.88E−24 128.8 287.1 197.0 85.8subcomponent, A chain 194 232311_PM_at 567 B2M Beta-2-microglobulin6.06E−24 42.3 118.6 83.7 35.2 195 219684_PM_at 64108 RTP4 receptor(chemosensory) 6.09E−24 63.1 129.3 93.7 50.4 transporter protein 4 196204057_PM_at 3394 IRF8 interferon regulatory factor 8 6.59E−24 89.8184.8 134.9 71.4 197 208296_PM_x_at 25816 TNFAIP8 tumor necrosis factor,alpha- 6.65E−24 136.9 242.5 195.1 109.6 induced protein 8 198204122_PM_at 7305 TYROBP TYRO protein tyrosine 6.73E−24 190.5 473.4332.8 143.3 kinase binding protein 199 224927_PM_at 170954 KIAA1949KIAA1949 6.87E−24 98.8 213.6 160.2 74.7

TABLE 11 Biopsy Expression Profiling of Kidney Transplants: 4-WayClassifier AR vs. ADNR vs. CAN vs. TX (Brazilian Samples) ValidationCohort Predictive Postive Negative Accuracy Sensitivity SpecificityPredictive Predictive Algorithm Predictors Comparison AUC (%) (%) (%)Value (%) Value (%) Nearest Centroid 199 AR vs. TX 0.976 98 100 95 95100 Nearest Centroid 199 ADNR vs. TX 1.000 100 100 100 100 100 NearestCentroid 199 CAN vs. TX 1.000 100 100 100 100 100

TABLE 12a Biopsy Expression Profiling of Kidney Transplants: 3-WayClassifier AR vs. ADNR vs. TX (TGCG Samples) Validation CohortPredictive Postive Negative Accuracy Sensitivity Specificity PredictivePredictive Algorithm Predictors Comparison AUC (%) (%) (%) Value (%)Value (%) Nearest Centroid 197 AR vs. TX 0.979 98 96 100 100 96 NearestCentroid 197 ADNR vs. TX 0.987 99 97 100 100 98 Nearest Centroid 197 ARvs. ADNR 0.968 97 100 93 95 100

TABLE 12b Biopsy Expression Profiling of Kidney Transplants: 3-WayClassifier AR vs. ADNR vs. TX (TGCG Samples) p-value Entrez Gene (FinalADNR- AR- TX- # Probeset ID Gene Symbol Gene Title Phenotype) Mean MeanMean  1 242956_PM_at 3417 IDH1 Isocitrate dehydrogenase 2.95E−22 32.729.9 53.6 1 (NADP+), soluble  2 208948_PM_s_at 6780 STAU1 staufen, RNAbinding 1.56E−29 1807.9 1531.8 2467.4 protein, homolog 1 (Drosophila)  3213037_PM_x_at 6780 STAU1 staufen, RNA binding 4.77E−27 1699.0 1466.82264.9 protein, homolog 1 (Drosophila)  4 207320_PM_x_at 6780 STAU1staufen, RNA binding 6.17E−28 1425.1 1194.6 1945.3 protein, homolog 1(Drosophila)  5 1555832_PM_s_at 1316 KLF6 Kruppel-like factor 6 5.82E−23899.4 1076.8 575.1  6 202376_PM_at 12 SERPINA3 serpin peptidaseinhibitor, 1.05E−25 186.2 387.1 51.7 clade A (alpha-1 antiproteinase,antitrypsin), member 3  7 226621_PM_at 9180 OSMR oncostatin M receptor1.28E−27 545.6 804.5 312.1  8 218983_PM_at 51279 C1RL complementcomponent 9.46E−25 167.1 244.5 99.4 1, r subcomponent-like  9215005_PM_at 54550 NECAB2 N-terminal EF-hand 1.95E−25 36.7 23.4 65.7calcium binding protein 2  10 202720_PM_at 26136 TES testis derivedtranscript 4.32E−24 285.4 379.0 204.2 (3 LIM domains)  11 240320_PM_at100131781 C14orf164 chromosome 14 open 1.64E−23 204.9 84.2 550.0 readingframe 164  12 243418_PM_at — — — 1.81E−24 56.3 31.1 104.8  13205466_PM_s_at 9957 HS3ST1 heparan sulfate 5.64E−24 73.6 123.8 42.1(glucosamine) 3-O- sulfotransferase 1  14 201042_PM_at 7052 TGM2transglutaminase 2 (C 3.87E−28 131.8 236.5 80.1 polypeptide, protein-glutamine-gamma- glutamyltransferase)  15 202018_PM_s_at 4057 LTFlactotransferrin 4.00E−25 392.3 1332.4 117.7  16 212503_PM_s_at 22982DIP2C DIP2 disco-interacting 6.62E−27 559.7 389.2 755.0 protein 2homolog C (Drosophila)  17 215049_PM_x_at 9332 CD163 CD163 molecule4.65E−23 232.8 481.3 112.9  18 209515_PM_s_at 5873 RAB27A RAB27A, memberRAS 2.11E−23 127.3 192.2 85.2 oncogene family  19 221932_PM_s_at 51218GLRX5 glutaredoxin 5 2.08E−22 1351.5 1145.8 1599.3  20 202207_PM_at10123 ARL4C ADP-ribosylation factor- 1.83E−29 106.9 258.6 57.2 like 4 C 21 227697_PM_at 9021 SOCS3 suppressor of cytokine 2.93E−23 35.9 69.119.9 signaling 3  22 227072_PM_at 25914 RTTN rotatin 4.26E−23 101.1 74.5132.9  23 201136_PM_at 5355 PLP2 proteolipid protein 2 2.71E−22 187.7274.0 131.7 (colonic epithelium- enriched)  24 212504_PM_at 22982 DIP2CDIP2 disco-interacting 2.57E−25 334.5 227.2 452.5 protein 2 homolog C(Drosophila)  25 200986_PM_at 710 SERPING1 serpin peptidase inhibitor,2.88E−26 442.7 731.5 305.3 clade G (C1 inhibitor), member 1  26203233_PM_at 3566 IL4R interleukin 4 receptor 6.30E−23 97.6 138.5 72.0 27 229295_PM_at 150166 IL17RA /// interleukin 17 receptor A 6.40E−2576.4 131.8 50.0 /// LOC150166 /// hypothetical protein 23765 LOC150166 28 231358_PM_at 83876 MRO maestro 1.11E−22 199.7 81.2 422.1  29201666_PM_at 7076 TIMP1 TIMP metallopeptidase 1.54E−22 1035.3 1879.4648.0 inhibitor 1  30 209774_PM_x_at 2920 CXCL2 chemokine (C-X-C motif)1.50E−26 24.9 52.9 15.5 ligand 2  31 217733_PM_s_at 9168 TMSB10 thymosinbeta 10 9.34E−27 4414.7 6331.3 3529.0  32 222939_PM_s_at 117247 SLC16A10solute carrier family 16, 2.55E−22 156.6 93.7 229.6 member 10 (aromaticamino acid transporter)  33 204924_PM_at 7097 TLR2 toll-like receptor 22.11E−22 96.8 162.0 66.6  34 225415_PM_at 151636 DTX3L deltex 3-like(Drosophila) 3.04E−24 230.2 376.4 166.9  35 202206_PM_at 10123 ARL4CADP-ribosylation factor- 1.16E−22 60.8 128.5 36.0 like 4 C  36213857_PM_s_at 961 CD47 CD47molecule 2.56E−27 589.6 858.0 481.2  37235529_PM_x_at 25939 SAMHD1 SAM domain and HD 4.49E−24 189.3 379.9 128.0domain 1  38 206693_PM_at 3574 IL7 interleukin 7 3.12E−22 37.3 57.0 28.9 39 219033_PM_at 79668 PARP8 poly (ADP-ribose) 1.88E−22 47.9 79.2 35.7polymerase family, member 8  40 201721_PM_s_at 7805 LAPTM5 lysosomalprotein 3.72E−24 396.5 934.6 249.4 transmembrane 5  41 204336_PM_s_at10287 RGS19 regulator of G-protein 3.52E−25 95.2 187.7 67.0 signaling 19 42 235964_PM_x_at 25939 SAMHD1 SAM domain and HD 2.45E−23 172.9 345.9117.5 domain 1  43 208091_PM_s_at 81552 VOPP1 vesicular, overexpressed9.47E−25 495.5 713.9 409.7 in cancer, prosurvival protein 1  44204446_PM_s_at 240 ALOX5 arachidonate 5- 6.25E−31 91.9 323.9 54.7lipoxygenase  45 212703_PM_at 83660 TLN2 talin 2 6.13E−23 270.8 159.7357.5  46 213414_PM_s_at 6223 RPS19 ribosomal protein S19 1.57E−224508.2 5432.1 4081.7  47 1565681_PM_s_at 22982 DIP2C DIP2disco-interacting 2.57E−22 66.4 35.7 92.5 protein 2 homolog C(Drosophila)  48 225764_PM_at 2120 ETV6 ets variant 6 2.63E−23 92.6133.0 77.0  49 227184_PM_at 5724 PTAFR platelet-activating factor1.73E−24 89.8 191.4 62.7 receptor  50 221840_PM_at 5791 PTPRE proteintyrosine 8.52E−23 71.0 133.2 51.8 phosphatase, receptor type, E  51225799_PM_at 112597 LOC541471 hypothetical LOC541471 1.18E−23 230.5444.3 172.0 /// /// /// non-protein coding 541471 NCRNA00152 RNA 152  52202957_PM_at 3059 HCLS1 hematopoietic cell- 1.94E−25 119.2 299.5 82.2specific Lyn substrate 1  53 229383_PM_at 55016 1-Marmembrane-associated ring 8.09E−25 33.8 88.0 22.9 finger (C3HC4) 1  5433304_PM_at 3669 ISG20 interferon stimulated 2.83E−27 33.2 101.3 22.1exonuclease gene 20 kDa  55 222062_PM_at 9466 IL27RA interleukin 27receptor, 1.79E−22 34.9 65.8 26.4 alpha  56 219574_PM_at 55016 1-Marmembrane-associated ring 6.65E−25 51.5 126.0 36.2 finger (C3HC4) 1  57202748_PM_at 2634 GBP2 guanylate binding protein 7.51E−26 196.7 473.0141.0 2, interferon-inducible  58 210895_PM_s_at 942 CD86 CD86 molecule3.80E−23 32.3 85.0 21.6  59 202208_PM_s_at 10123 ARL4C ADP-ribosylationfactor- 1.24E−23 54.0 99.6 42.2 like 4 C  60 221666_PM_s_at 29108 PYCARDPYD and CARD domain 5.55E−24 60.7 132.8 44.7 containing  61 227125_PM_at3455 IFNAR2 interferon (alpha, beta and 3.57E−24 70.0 126.2 55.8 omega)receptor 2  62 226525_PM_at 9262 STK17B serine/threonine kinase 3.43E−24146.8 338.7 107.6 17 b  63 210644_PM_s_at 3903 LAIR1leukocyte-associated 2.96E−24 29.7 74.6 21.2 immunoglobulin-likereceptor 1  64 230391_PM_at 8832 CD84 CD84 molecule 1.89E−22 47.9 130.132.5  65 242907_PM_at — — — 2.54E−22 59.3 165.1 39.8  66 1553906_PM_s_at221472 FGD2 FYVE, RhoGEF and PH 1.67E−26 104.6 321.0 71.9 domaincontaining 2  67 223922_PM_x_at 64231 MS4A6A membrane-spanning 4-8.68E−24 289.2 656.8 214.5 domains, subfamily A, member 6 A  68230550_PM_at 64231 MS4A6A membrane-spanning 4- 7.63E−24 44.8 124.3 30.9domains, subfamily A, member 6 A  69 202953_PM_at 713 C1QB complementcomponent 2.79E−22 215.8 638.4 142.5 1, q subcomponent, B chain  70213733_PM_at 4542 MYO1F myosin IF 2.25E−23 26.8 52.7 20.9  71204774_PM_at 2123 EVI2A ecotropic viral integration 5.10E−24 63.6 168.544.9 site 2 A  72 211366_PM_x_at 834 CASP1 caspase 1, apoptosis-3.40E−24 115.3 261.0 87.1 related cysteine peptidase (interleukin 1,beta, convertase)  73 204698_PM_at 3669 ISG20 interferon stimulated1.27E−28 41.5 165.1 27.6 exonuclease gene 20 kDa  74 201762_PM_s_at 5721PSME2 proteasome (prosome, 2.31E−22 1251.6 1825.1 1091.0 macropain)activator subunit 2 (PA28 beta)  75 204470_PM_at 2919 CXCL1 chemokine(C-X-C motif) 2.81E−24 22.9 63.7 16.2 ligand 1 (melanoma growthstimulating activity, alpha)  76 242827_PM_x_at — — — 9.00E−23 22.1 52.316.3  77 209970_PM_x_at 834 CASP1 caspase 1, apoptosis- 4.40E−25 116.0266.6 88.7 related cysteine peptidase (interleukin 1, beta, convertase) 78 228532_PM_at 128346 C1orf162 chromosome 1 open 1.57E−22 82.6 217.760.0 reading frame 162  79 232617_PM_at 1520 CTSS cathepsin S 2.83E−23209.6 537.9 154.8  80 203761_PM_at 6503 SLA Src-like-adaptor 3.80E−2369.6 179.2 51.4  81 219666_PM_at 64231 MS4A6A membrane-spanning 4-4.29E−23 159.2 397.6 118.7 domains, subfamily A, member 6 A  82223280_PM_x_at 64231 MS4A6A membrane-spanning 4- 2.53E−24 269.0 711.8199.5 domains, subfamily A, member 6 A  83 225701_PM_at 80709 AKNAAT-hook transcription 2.87E−29 37.7 102.8 29.0 factor  84 224356_PM_x_at64231 MS4A6A membrane-spanning 4- 1.56E−23 150.6 399.6 111.2 domains,subfamily A, member 6 A  85 202643_PM_s_at 7128 TNFAIP3 tumor necrosisfactor, 9.92E−24 43.7 92.8 34.8 alpha-induced protein 3  86202644_PM_s_at 7128 TNFAIP3 tumor necrosis factor, 2.12E−27 169.8 380.4136.6 alpha-induced protein 3  87 213566_PM_at 6039 RNASE6 ribonuclease,RNase A 1.55E−23 180.9 482.0 134.3 family, k6  88 219386_PM_s_at 56833SLAMF8 SLAM family member 8 1.22E−22 28.2 92.1 19.4  89 203416_PM_at 963CD53 CD53 molecule 3.13E−23 215.9 603.0 157.8  90 200003_PM_s_at 6158RPL28 ribosomal protein L28 1.73E−22 4375.2 5531.2 4069.9  91206420_PM_at 10261 IGSF6 immunoglobulin 5.65E−23 45.1 131.5 32.7superfamily, member 6  92 217028_PM_at 7852 CXCR4 chemokine (C-X-Cmotif) 6.84E−27 100.8 304.4 75.3 receptor 4  93 232024_PM_at 26157GIMAP2 GTPase, IMAP family 2.94E−24 90.2 197.7 72.5 member 2  94238327_PM_at 440836 ODF3B outer dense fiber of sperm 1.75E−27 32.8 81.426.1 tails 3 B  95 209083_PM_at 11151 CORO1A coronin, actin binding2.78E−27 46.9 163.8 34.3 protein, 1 A  96 232724_PM_at 64231 MS4A6Amembrane-spanning 4- 6.28E−23 24.8 47.6 20.7 domains, subfamily A,member 6 A  97 211742_PM_s_at 2124 EVI2B ecotropic viral integration1.56E−22 73.9 236.5 53.2 site 2 B  98 202659_PM_at 5699 PSMB10proteasome (prosome, 2.29E−24 180.7 355.6 151.5 macropain) subunit, betatype, 10  99 226991_PM_at 4773 NFATC2 nuclear factor of activated2.42E−23 37.9 87.0 30.3 T-cells, cytoplasmic, calcineurin-dependent 2100 210538_PM_s_at 330 BIRC3 baculoviral IAP repeat- 5.40E−26 106.7276.7 84.6 containing 3 101 205269_PM_at 3937 LCP2 lymphocyte cytosolic7.35E−25 30.2 92.2 22.9 protein 2 (SH2 domain containing leukocyteprotein of 76 kDa) 102 211368_PM_s_at 834 CASP1 caspase 1, apoptosis-9.43E−27 102.6 274.3 81.8 related cysteine peptidase (interleukin 1,beta, convertase) 103 205798_PM_at 3575 IL7R interleukin 7 receptor1.57E−24 44.1 136.8 33.4 104 228442_PM_at 4773 NFATC2 nuclear factor ofactivated 5.58E−23 39.0 84.8 32.0 T-cells, cytoplasmic,calcineurin-dependent 2 105 213603_PM_s_at 5880 RAC2 ras-related C3botulinum 3.38E−25 113.9 366.5 86.5 toxin substrate 2 (rho family, smallGTP binding protein Rac2) 106 228964_PM_at 639 PRDM1 PR domaincontaining 1, 1.42E−23 21.2 52.1 17.0 with ZNF domain 107 209606_PM_at9595 CYTIP cytohesin 1 interacting 1.66E−26 41.4 114.3 32.9 protein 108214022_PM_s_at 8519 IFITM1 interferon induced 1.61E−23 799.1 1514.7683.3 transmembrane protein 1 (9-27) 109 202307_PM_s_at 6890 TAP1transporter 1, ATP- 8.61E−26 172.4 420.6 141.0 binding cassette, sub-family B (MDR/TAP) 110 204882_PM_at 9938 ARHGAP25 Rho GTPase activating8.14E−23 51.5 107.3 43.0 protein 25 111 227344_PM_at 10320 IKZF1 IKAROSfamily zinc 5.30E−26 17.8 40.0 14.9 finger 1 (Ikaros) 112 205270_PM_s_at3937 LCP2 lymphocyte cytosolic 3.75E−24 56.8 162.6 44.6 protein 2 (SH2domain containing leukocyte protein of 76 kDa) 113 223640_PM_at 10870HCST hematopoietic cell signal 5.55E−23 91.0 234.0 72.7 transducer 114226218_PM_at 3575 IL7R interleukin 7 receptor 2.15E−23 55.6 197.0 41.4115 226219_PM_at 257106 ARHGAP30 Rho GTPase activating 1.87E−26 46.4127.9 37.5 protein 30 116 38149_PM_at 9938 ARHGAP25 Rho GTPaseactivating 1.20E−23 51.3 108.9 43.2 protein 25 117 213975_PM_s_at 4069LYZ lysozyme 2.70E−22 458.4 1626.0 338.1 118 238668_PM_at — — — 1.35E−2318.2 49.2 14.5 119 200887_PM_s_at 6772 STAT1 signal transducer and1.27E−22 1141.7 2278.6 972.9 activator of transcription 1.91 kDa 1201555756_PM_a_at 64581 CLEC7A C-type lectin domain 2.11E−22 16.6 45.413.2 family 7, member A 121 205039_PM_s_at 10320 IKZF1 IKAROS familyzinc 6.73E−23 29.1 65.9 24.2 finger 1 (Ikaros) 122 206011_PM_at 834CASP1 caspase 1, apoptosis- 6.34E−25 74.5 198.0 60.7 related cysteinepeptidase (interleukin 1, beta, convertase) 123 221698_PM_s_at 64581CLEC7A C-type lectin domain 9.79E−24 61.5 164.6 49.7 family 7, memberA124 227346_PM_at 10320 IKZF1 IKAROS family zinc 1.51E−26 24.9 79.7 19.8finger 1 (Ikaros) 125 230499_PM_at — — — 8.55E−23 29.7 68.7 24.7 126229391_PM_s_at 441168 FAM26F family with sequence 2.74E−23 98.6 379.773.7 similarity 26, member F 127 205159_PM_at 1439 CSF2RB colonystimulating factor 1.74E−23 33.7 107.5 26.3 2 receptor, beta, low-affinity (granulocyte- macrophage) 128 205639_PM_at 313 AOAH acyloxyacylhydrolase 4.43E−23 30.3 72.6 25.2 (neutrophil) 129 204563_PM_at 6402SELL selectin L 1.00E−23 40.7 134.7 31.7 130 201288_PM_at 397 ARHGDIBRho GDP dissociation 1.92E−22 354.9 686.8 308.1 inhibitor (GDI) beta 131209969_PM_s_at 6772 STAT1 signal transducer and 5.49E−24 395.8 1114.5320.8 activator of transcription 1.91 kDa 132 229390_PM_at 441168 FAM26Ffamily with sequence 3.91E−25 103.8 520.0 75.9 similarity 26, member F133 242916_PM_at 11064 CEP110 centrosomal protein 1.99E−23 30.8 68.126.2 110 kDa 134 207651_PM_at 29909 GPR171 G protein-coupled 1.90E−2925.8 89.9 20.9 receptor 171 135 229937_PM_x_at 10859 LILRB1 Leukocyte1.67E−24 23.5 79.9 18.5 immunoglobulin-like receptor, subfamily B (withTM and ITIM domains), member 136 232543_PM_x_at 64333 ARHGAP9 RhoGTPaseactivating 3.66E−26 31.7 99.1 25.8 protein 9 137 203332_PM_s_at 3635INPP5D inositol polyphosphate-5- 3.66E−24 27.9 60.5 24.0 phosphatase,145 kDa 138 213160_PM_at 1794 DOCK2 dedicator of cytokinesis 2 1.03E−2433.7 92.6 27.8 139 204912_PM_at 3587 IL10RA interleukin 10 receptor,1.28E−24 57.0 178.7 46.1 alpha 140 204205_PM_at 60489 APOBEC3Gapolipoprotein B mRNA 6.40E−27 95.4 289.4 78.7 editing enzyme, catalyticpolypeptide-like 3 G 141 206513_PM_at 9447 AIM2 absent in melanoma 29.67E−23 17.2 50.7 13.9 142 203741_PM_s_at 113 ADCY7 adenylate cyclase 72.34E−22 23.6 59.3 19.7 143 206118_PM_at 6775 STAT4 signal transducerand 8.28E−26 21.0 49.5 18.1 activator of transcription 4 144227677_PM_at 3718 JAK3 Janus kinase 3 6.48E−24 18.6 51.7 15.5 145227353_PM_at 147138 TMC8 transmembrane channel- 6.49E−29 19.8 64.2 16.6like 8 146 1552701_PM_a_at 114769 CARD16 caspase recruitment 2.52E−23143.8 413.6 119.8 domain family, member 16 147 1552703_PM_s_at 114769CARD16 caspase recruitment 7.57E−24 64.6 167.8 54.9 /// 834 /// CASP1domain family, member 16 /// caspase 1, apoptosis-related cysteine 148229437_PM_at 114614 MIR155HG MIR155 host gene (non- 3.90E−27 15.4 50.412.9 protein coding) 149 204319_PM_s_at 6001 RGS10 regulator ofG-protein 6.54E−24 159.7 360.4 139.4 signaling 10 150 204118_PM_at 962CD48 CD48 molecule 3.85E−23 82.9 341.5 65.2 151 1559584_PM_a_at 283897C16orf54 chromosome 16 open 2.86E−24 31.3 95.8 26.1 reading frame 54 152212588_PM_at 5788 PTPRC protein tyrosine 2.46E−22 94.4 321.0 76.4phosphatase, receptor type, C 153 219014_PM_at 51316 PLAC8placenta-specific 8 2.03E−23 38.8 164.1 30.7 154 235735_PM_at — — —1.39E−25 13.0 34.9 11.2 155 203932_PM_at 3109 HLA-DMB majorhistocompatibility 6.25E−23 422.4 853.9 376.0 complex, class II, DM beta156 223502_PM_s_at 10673 TNFSF13B tumor necrosis factor 2.62E−23 73.0244.3 60.0 (ligand) superfamily, member 13 b 157 1405_PM_i_at 6352 CCL5chemokine (C-C motif) 2.22E−25 68.0 295.7 54.6 ligand 5 158 226474_PM_at84166 NLRC5 NLR family, CARD 1.33E−23 64.4 173.5 55.1 domain containing5 159 204220_PM_at 9535 GMFG glia maturation factor, 1.56E−23 147.0339.3 128.9 gamma 160 204923_PM_at 54440 SASH3 SAM and SH3 domain4.56E−23 25.4 68.2 21.7 containing 3 161 206082_PM_at 10866 HCP5 HLAcomplex P5 1.23E−23 76.2 185.1 66.6 162 204670_PM_x_at 3123 /// HLA-major histocompatibility 1.31E−23 2461.9 4344.6 2262.0 3126 DRB1 ///complex, class II, DR beta HLA- 1 /// major DRB4 histocompatibility comp163 228869_PM_at 124460 SNX20 sorting nexin 20 2.59E−22 25.7 67.3 22.2164 205831_PM_at 914 CD2 CD2 molecule 2.30E−27 40.4 162.5 33.9 165206978_PM_at 729230 CCR2 chemokine (C-C motif) 4.46E−23 32.1 100.7 27.3receptor 2 166 224451_PM_x_at 64333 ARHGAP9 Rho GTPase activating4.23E−24 34.2 103.4 29.4 protein 9 167 204279_PM_at 5698 PSMB9proteasome (prosome, 2.81E−23 241.6 637.4 211.3 macropain) subunit, betatype, 9 (large multifunctional peptidase 168 209795_PM_at 969 CD69 CD69molecule 5.81E−27 17.6 57.6 15.2 169 229625_PM_at 115362 GBP5 guanylatebinding 5.85E−24 29.3 133.3 24.0 protein 5 170 213416_PM_at 3676 ITGA4integrin, alpha 4 (antigen 1.19E−23 26.3 78.1 22.8 CD49D, alpha 4subunit of VLA-4 receptor) 171 206804_PM_at 917 CD3G CD3g molecule,gamma 1.50E−27 19.7 60.6 17.3 (CD3-TCR complex) 172 222895_PM_s_at 64919BCL11B B-cell CLL/lymphoma 7.01E−23 22.0 64.5 19.1 11 B (zinc fingerprotein) 173 211582_PM_x_at 7940 LST1 leukocyte specific 2.13E−22 57.5183.8 49.4 transcript 1 174 1555852_PM_at 100507463 LOC100507463hypothetical 8.92E−26 78.9 202.6 70.9 LOC100507463 175 213539_PM_at 915CD3D CD3d molecule, delta 9.01E−27 70.8 335.1 60.0 (CD3-TCR complex) 176239237_PM_at — — — 9.82E−24 15.9 34.9 14.5 177 229723_PM_at 117289 TAGAPT-cell activation 5.21E−24 29.2 82.9 26.2 RhoGTPase activating protein178 204655_PM_at 6352 CCL5 chemokine (C-C motif) 7.63E−24 77.5 339.466.8 ligand 5 179 229041_PM_s_at — — — 1.64E−24 36.5 132.1 32.4 180205267_PM_at 5450 POU2AF1 POU class 2 associating 4.47E−23 17.9 86.815.7 factor 1 181 226878_PM_at 3111 HLA-DOA major histocompatibility2.59E−25 102.0 288.9 94.3 complex, class II, DO alpha 182 205488_PM_at3001 GZMA granzyme A (granzyme 1, 4.14E−25 37.3 164.8 33.4 cytotoxicT-lymphocyte- associated serine esterase 3) 183 201137_PM_s_at 3115 HLA-major histocompatibility 2.17E−22 1579.1 3863.7 1475.9 DPB1 complex,class II, DP beta 1 184 204891_PM_s_at 3932 LCK lymphocyte-specific7.95E−28 19.3 74.2 17.8 protein tyrosine kinase 185 231776_PM_at 8320EOMES eomesodermin 1.71E−23 24.0 63.9 22.4 186 211339_PM_s_at 3702 ITKIL2-inducible T-cell 7.40E−23 16.7 44.4 15.7 kinase 187 223322_PM_at83593 RASSF5 Ras association 5.21E−26 41.5 114.4 39.2 (RalGDS/AF-6)domain family member 5 188 205758_PM_at 925 CD8A CD8a molecule 2.80E−2524.2 105.8 22.2 189 231124_PM_x_at 4063 LY9 lymphocyte antigen 92.81E−23 16.7 45.8 15.7 190 211796_PM_s_at 28638 TRBC1 /// T cellreceptor beta 4.38E−25 69.2 431.5 63.6 /// TRBC2 constant 1 /// T cell28639 receptor beta constant 2 191 210915_PM_x_at 28638 TRBC2 T cellreceptor beta 1.91E−27 39.7 230.7 37.5 constant 2 192 205821_PM_at 22914KLRK1 killer cell lectin-like 1.18E−25 30.3 111.5 31.3 receptorsubfamily K, member 1 193 236295_PM_s_at 197358 NLRC3 NLR family, CARD6.22E−26 19.0 52.5 18.6 domain containing 3 194 213193_PM_x_at 28639TRBC1 T cell receptor beta 4.22E−25 95.3 490.9 92.1 constant 1 195211656_PM_x_at 100133 HLA- major histocompatibility 5.98E−25 211.3 630.2208.2 583 /// DQB1 /// complex, class II, DQ 3119 LOC100133583 beta 1/// HLA class II histocompatibili 196 209670_PM_at 28755 TRAC T cellreceptor alpha 1.44E−24 37.9 149.9 38.5 constant 197 213888_PM_s_at80342 TRAF3IP3 TRAF3 interacting 1.66E−22 30.8 101.9 30.5 protein 3

TABLE 13 Biopsy Expression Profiling of Kidney Transplants: 3-WayClassifier AR vs. ADNR vs. TX (Brazilian Samples) Validation CohortPredictive Postive Negative Accuracy Sensitivity Specificity PredictivePredictive Algorithm Predictors Comparison AUC (%) (%) (%) Value (%)Value (%) Nearest Centroid 197 AR vs. TX 0.976 98 100 95 95 100 NearestCentroid 197 ADNR vs. TX 1.000 100 100 100 100 100 Nearest Centroid 197AR vs. ADNR 0.962 97 100 91 95 100

TABLE 14a Biopsy Expression Profiling of Kidney Transplants: 2-WayClassifier CAN vs. TX (TGCG Samples) Validation Cohort PredictivePostive Negative Accuracy Sensitivity Specificity Predictive PredictiveAlgorithm Predictors Comparison AUC (%) (%) (%) Value (%) Value (%)Nearest Centroid 200 AR vs. TX 0.965 97 96 97 96 97

TABLE 14b Biopsy Expression Profiling of Kidney Transplants. 2-WayClassifier CAN vs. TX p-value (Final Entrez Gene Pheno- CAN- TX- #Probeset ID Gene Symbol Gene Title type) Mean Mean  1 204698_PM_at 3669ISG20 interferon stimulated 1.93E−19 96.1 27.6 exonuclease gene 20 kDa 2 33304_PM_at 3669 ISG20 interferon stimulated 2.02E−19 63.4 22.1exonuclease gene 20 kDa  3 217022_PM_s_at 100126 IGHA1 ///immunoglobulin heavy 4.31E−19 494.6 49.4 583 /// IGHA2 /// constantalpha 1 /// 3493 /// LOC100126583 immunoglobulin heavy 3494 constantalpha 2 (A2m ma  4 202957_PM_at 3059 HCLS1 hematopoietic cell-specific5.13E−19 229.9 82.2 Lyn substrate 1  5 203761_PM_at 6503 SLASrc-like-adaptor 1.10E−18 138.4 51.4  6 204446_PM_s_at 240 ALOX5arachidonate 5-lipoxygen ase 1.36E−18 216.7 54.7  7 209198_PM_s_at 23208SYT11 synaptotagmin XI 1.93E−18 41.8 22.8  8 228964_PM_at 639 PRDM1 PRdomain containing 1, 2.37E−18 41.5 17.0 with ZNF domain  9 201042_PM_at7052 TGM2 transglutaminase 2 (C 3.13E−18 172.6 80.1 polypeptide,protein- glutamine-gamma- glutamyltransferase)  10 226219_PM_at 257106ARHGAP30 Rho GTPase activating 7.21E−18 91.8 37.5 protein 30  11225701_PM_at 80709 AKNA AT-hook transcription 7.27E−18 73.2 29.0 factor 12 202207_PM_at 10123 ARL4C ADP-ribosylation factor- 7.98E−18 190.457.2 like 4 C  13 219574_PM_at 55016 MAR1 membrane-associated ring8.98E−18 87.5 36.2 finger (C3HC4) 1  14 209083_PM_at 12-Jul CORO1Acoronin, actin binding 1.06E−17 107.1 34.3 protein, 1 A  15 226621_PM_at9180 OSMR oncostatin M receptor 1.85E−17 682.9 312.1  16 1405_PM_i_at6352 CCL5 chemokine (C-C motif) 2.19E−17 195.6 54.6 ligand 5  17213160_PM_at 1794 DOCK2 dedicator of cytokinesis 2 2.75E−17 66.0 27.8 18 227346_PM_at 10320 IKZF1 IKAROS family zinc finger 2.92E−17 53.319.8 1 (Ikaros)  19 204205_PM_at 60489 APOBEC3G apolipoprotein B mRNA2.92E−17 192.0 78.7 editing enzyme, catalytic polypeptide-like 3 G  20218322_PM_s_at 51703 ACSL5 acyl-CoA synthetase long- 3.15E−17 84.5 48.1chain family member  21 238327_PM_at 440836 ODF3B outer dense fiber ofsperm 3.42E−17 58.5 26.1 tails 3 B  22 218983_PM_at 51279 C1RLcomplement component 1, r 4.33E−17 206.9 99.4 subcomponent-like  23210538_PM_s_at 330 BIRC3 baculoviral IAP repeat- 4.49E−17 199.1 84.6containing 3  24 207651_PM_at 29909 GPR171 G protein-coupled receptor5.48E−17 57.0 20.9 171  25 201601_PM_x_at 8519 IFITM1 interferon induced6.07E−17 2202.5 1251.1 transmembrane protein 1 (9-27)  26 226878_PM_at3111 HLA-DOA major histocompatibility 6.12E−17 201.4 94.3 complex, classII, DO alpha  27 1555756_PM_a_at 64581 CLEC7A C-type lectin domainfamily 6.22E−17 28.9 13.2 7, member A  28 1559584_PM_a_at 283897C16orf54 chromosome 16 open 6.73E−17 71.5 26.1 reading frame 54  29209795_PM_at 969 CD69 CD69 molecule 9.46E−17 40.6 15.2  30 230550_PM_at64231 MS4A6A membrane-spanning 4- 1.20E−16 88.0 30.9 domains, subfamilyA, member 6 A  31 1553906_PM_s_at 221472 FGD2 FYVE, RhoGEF and PH1.34E−16 219.3 71.9 domain containing 2  32 205798_PM_at 3575 IL7Rinterleukin 7 receptor 1.55E−16 106.3 33.4  33 1555852_PM_at 100507463LOC100507463 hypothetical 1.81E−16 154.2 70.9 LOC100507463  34224916_PM_at 340061 TMEM173 transmembrane protein 173 1.84E−16 67.8 40.0 35 211368_PM_s_at 834 CASP1 caspase 1, apoptosis-related 1.85E−16 191.481.8 cysteine peptidase (interleukin 1, beta, convertase)  36226474_PM_at 84166 NLRC5 NLRC5 family, CARD 1.85E−16 129.8 55.1 domaincontaining 5  37 201137_PM_s_at 3115 HLA- major histocompatibility1.90E−16 3151.7 1475.9 DPB1 complex, class II, DP beta 1  38210785_PM_s_at 9473 C1orf38 chromosome 1 open reading 2.07E−16 39.9 16.4frame 38  39 215121_PM_x_at 100290 IGLC7 /// immunoglobulin lambda2.13E−16 1546.8 250.4 481 /// IGLV1-44 constant 7 /// 28823 ///immunoglobulin lambda /// LOC100290481 variable 1-44 /// 28834immunoglob  40 1555832_PM_s_at 1316 KLF6 Kruppel-like factor 6 2.35E−161003.3 575.1  41 221932_PM_s_at 51218 GLRX5 glutaredoxin 5 2.49E−161218.1 1599.3  42 207677_PM_s_at 4689 NCF4 neutrophil cytosolic factor2.65E−16 39.5 19.2 4.40 kDa  43 202720_PM_at 26136 TES testis derivedtranscript (3 2.68E−16 357.9 204.2 LIM domains)  44 220005_PM_at 53829P2RY13 purinergic receptor P2Y, G- 2.72E−16 29.6 14.8 protein coupled,13  45 200904_PM_at 3133 HLA-E major histocompatibility 2.73E−16 1607.7994.7 complex, class I, E  46 222294_PM_s_at 5873 RAB27A RAB27A, memberRAS 2.91E−16 263.0 146.4 oncogene family  47 205831_PM_at 914 CD2 CD2molecule 3.32E−16 100.9 33.9  48 227344_PM_at 10320 IKZF1 IKAROS familyzinc finger 3.39E−16 28.5 14.9 1 (Ikaros)  49 209374_PM_s_at 3507 IGHMimmunoglobulin heavy 3.73E−16 301.0 45.0 constant mu  50 202307_PM_s_at6890 TAP1 transporter 1, ATP-binding 4.84E−16 280.0 141.0 cassette,sub-family B (MDR/TAP)  51 223218_PM_s_at 64332 NFKBIZ nuclear factor ofkappa light 5.05E−16 399.9 159.1 polypeptide gene enhancer in B-cellsinhibitor, zeta  52 229437_PM_at 114614 MIR155HG MIR155 host gene (non-5.85E−16 28.5 12.9 protein coding)  53 213603_PM_s_at 5880 RAC2ras-related C3 botulinum 5.98E−16 250.3 86.5 toxin substrate 2 (rhofamily, small GTP binding protein Rac2)  54 214669_PM_x_at 3514 /// IGK@/// immunoglobulin kappa 6.32E−16 3449.1 587.1 50802 IGKC locus ///immunoglobulin kappa constant  55 211430_PM_s_at 28396 IGHG1 ///immunoglobulin heavy 6.39E−16 2177.7 266.9 /// 3500 IGHM /// constantgamma 1 (G1m /// 3507 IGHV4- marker) /// immunoglobulin 31 heavyconstant mu  56 228471_PM_at 91526 ANKRD4 ankyrin repeat domain 446.42E−16 184.6 86.5  57 209138_PM_x_at 3535 IGL@ Immunoglobulin lambda7.54E−16 2387.0 343.2 locus  58 227353_PM_at 147138 TMC8 transmembranechannel- 8.01E−16 42.7 16.6 like 8  59 200986_PM_at 710 SERPING1 serpinpeptidase inhibitor, 8.10E−16 590.2 305.3 clade G (C1 inhibitor), member1  60 212203_PM_x_at 10410 IFITM3 interferon induced 8.17E−16 4050.12773.8 transmembrane protein 3 (1-8U)  61 221651_PM_x_at 3514 //// IGK@/// immunoglobulin kappa 9.60E−16 3750.2 621.2 50802 IGKC locus ///immunoglobulin kappa constant  62 214836_PM_x_at 28299 IGK@ ///immunoglobulin kappa 9.72E−16 544.2 109.1 /// 3514 IGKC /// locus ///immunoglobulin /// IGKV1-5 kappa constant /// 50802 immunoglobulin kappav  63 1552703_PM_s_at 114769 CARD16 caspase recruitment domain 1.12E−15120.6 54.9 /// 834 /// CASP1 family, member 16 /// caspase 1,apoptosis-related cysteine  64 202901_PM_x_at 1520 CTSS cathepsin S1.13E−15 130.9 45.1  65 215379_PM_x_at 28823 IGLC7 /// immunoglobulinlambda 1.16E−15 1453.0 248.1 /// IGLV1-44 constant 7 /// 28834immunoglobulin lambda variable 1-44  66 222939_PM_s_at 117247 SLC16A10solute carrier family 16, 1.22E−15 115.1 229.6 member 10 (aromatic aminoacid transporter)  67 232617_PM_at 1520 CTSS cathepsin S 1.22E−15 392.2154.8  68 235964_PM_x_at 25939 SAMHD1 SAM domain and HD 1.26E−15 270.1117.5 domain 1  69 205159_PM_at 1439 CSF2RB colony 1.28E−15 70.4 26.3stimulating factor 2 receptor, beta, low-affinity(granulocyte-macrophage)  70 224451_PM_x_at 64333 ARHGAP9 Rho GTPaseactivating 1.34E−15 71.8 29.4 protein 9  71 214677_PM_x_at 100287 IGL@/// Immunoglobulin lambda 1.35E−15 2903.1 433.7 927 /// LOC100287927locus /// Hypothetical 3535 protein LOC100287927  72 217733_PM_s_at 9168TMSB10 thymosin beta 10 1.37E−15 5555.2 3529.0  73 38149_PM_at 9938ARHGAP25 Rho GTPase activating 1.46E−15 83.2 43.2 protein 25  74221671_PM_x_at 3514 /// IGK@ /// immunoglobulin kappa 1.57E−15 3722.5642.9 50802 IGKC locus /// immunoglobulin kappa constant  75214022_PM_s_at 8519 IFITM1 interferon induced 1.59E−15 1236.6 683.3transmembrane protein 1 (9-27)  76 223217_PM_s_at 64332 NFKBIZ nuclearfactor of kappa light 1.61E−15 196.6 79.7 polypeptide gene enhancer inB-cells inhibitor, zeta  77 206118_PM_at 6775 STAT4 signal transducerand 1.67E−15 37.7 18.1 activator of transcription 4  78 221666_PM_s_at29108 PYCARD PYD and CARD domain 1.82E−15 95.5 44.7 containing  79207375_PM_s_at 3601 IL15RA interleukin 15 receptor, 1.94E−15 51.2 28.2alpha  80 209197_PM_at 23208 SYT11 synaptotagmin XI 2.02E−15 38.2 24.9 81 243366_PM_s_at — — — 2.05E−15 52.5 22.0  82 224795_PM_x_at 3514 ///IGK@ /// immunoglobulin kappa 2.18E−15 3866.2 670.5 50802 IGKC locus ///immunoglobulin kappa constant  83 36711_PM_at 23764 MAFF v-mafmusculoaponeurotic 2.26E−15 113.0 40.7 fibrosarcoma oncogene homolog F(avian)  84 227125_PM_at 3455 IFNAR2 interferon (alpha, beta and2.27E−15 96.7 55.8 omega) receptor 2  85 235735_PM_at — — — 2.58E−1524.5 11.2  86 209515_PM_s_at 5873 RAB27A RAB27A, member RAS 2.61E−15160.5 85.2 oncogene family  87 204670_PM_x_at 3123 /// HLA- major2.61E−15 3694.9 2262.0 3126 DRB1 /// histocompatibility HLA- complex,class II, DR beta 1 DRB4 /// major histocompatibility comp  88205269_PM_at 3937 LCP2 lymphocyte cytosolic 2.85E−15 58.9 22.9 protein 2(SH2 domain containing leukocyte protein of 76 kDa)  89 226525_PM_at9262 STK17B serine/threonine kinase 17 b 3.00E−15 259.1 107.6  90229295_PM_at 150166 IL17RA interleukin 17 receptor A /// 3.02E−15 98.450.0 /// /// hypothetical protein 23765 LOC150166 LOC150166  91206513_PM_at 9447 AIM2 absent in melanoma 2 3.18E−15 30.5 13.9  92209774_PM_x_at 2920 CXCL2 chemokine (C-X-C motif) 3.45E−15 38.2 15.5ligand 2  93 211656_PM_x_at 100133 HLA- major histocompatibility3.51E−15 459.8 208.2 583 /// DQB1 /// complex, class II, DQ beta 1 3119LOC100133583 /// HLA class II histocompatibili  94 206011_PM_at 834CASP1 caspase 1, apoptosis-related 3.56E−15 146.2 60.7 cysteinepeptidase (interleukin 1, beta, convertase)  95 202803_PM_s_at 3689ITGB2 integrin, beta 2 3.68E−15 182.6 65.2 (complement component 3receptor 3 and 4 subunit)  96 221698_PM_s_at 64581 CLEC7A C-type3.69E−15 112.8 49.7 lectin domain family 7, member A  97 229937_PM_x_at10859 LILRB1 Leukocyte immunoglobulin- 3.75E−15 50.0 18.5 like receptor,subfamily B (with TM and ITIM domains), member  98 235529_PM_x_at 25939SAMHD1 SAM domain and HD 3.99E−15 289.1 128.0 domain 1  99 223322_PM_at83593 RASSF5 Ras association 4.03E−15 79.3 39.2 (RalGDS/AF-6) domainfamily member 5 100 211980_PM_at 1282 COL4A1 collagen, type IV, alpha 14.75E−15 1295.8 774.7 101 201721_PM_s_at 7805 LAPTM5 lysosomal protein4.83E−15 661.3 249.4 transmembrane 5 102 242916_PM_at 11064 CEP110centrosomal protein 110 kDa 4.89E−15 51.2 26.2 103 206978_PM_at 729230CCR2 chemokine (C-C motif) 5.01E−15 68.6 27.3 receptor 2 104244353_PM_s_at 154091 SLC2A12 solute carrier family 2 5.72E−15 51.6100.5 (facilitated glucose transporter), member 12 105 215049_PM_x_at9332 CD163 CD163 molecule 6.21E−15 344.5 112.9 106 1552510_PM_at 142680SLC34A3 solute carrier family 34 6.40E−15 95.6 206.6 (sodium phosphate),member 3 107 225636_PM_at 6773 STAT2 signal transducer and 6.63E−15711.5 485.3 activator of transcription 2, 113 kDa 108 229390_PM_at441168 FAM26F family with sequence 6.73E−15 272.4 75.9 similarity 26,member F 109 235229_PM_at — — — 6.90E−15 135.0 41.9 110 226218_PM_at3575 IL7R interleukin 7 receptor 7.22E−15 147.1 41.4 111 217028_PM_at7852 CXCR4 chemokine (C-X-C motif) 7.40E−15 208.4 75.3 receptor 4 112204655_PM_at 6352 CCL5 chemokine (C-C motif) 8.57E−15 223.4 66.8 ligand5 113 227184_PM_at 5724 PTAFR platelet-activating factor 8.78E−15 134.462.7 receptor 114 202748_PM_at 2634 GBP2 guanylate binding protein 2,8.91E−15 306.7 141.0 interferon-inducible 115 226991_PM_at 4773 NFATC2nuclear factor of activated 9.05E−15 66.7 30.3 T-cells, cytoplasmic,calcineurin-dependent 2 116 216565_PM_x_at — — — 9.49E−15 1224.6 779.1117 203104_PM_at 1436 CSF1R colony stimulating factor 1 9.57E−15 42.722.1 receptor 118 238668_PM_at — — — 9.84E−15 33.6 14.5 119 204923_PM_at54440 SASH3 SAM and SH3 domain 9.93E−15 47.6 21.7 containing 3 120230036_PM_at 219285 SAMD9L sterile alpha motif domain 1.02E−14 128.072.7 containing 9-like 121 211742_PM_s_at 2124 EVI2B ecotropic viralintegration 1.03E−14 166.2 53.2 site 2 B 122 236782_PM_at 154075 SAMD3sterile alpha motif domain 1.11E−14 23.3 13.3 containing 3 123232543_PM_x_at 64333 ARHGAP9 Rho GTPase activating 1.13E−14 62.3 25.8protein 9 124 231124_PM_x_at 4063 LY9 lymphocyte antigen 9 1.18E−14 33.715.7 125 215946_PM_x_at 3543 /// IGLL1 /// immunoglobulin lambda-1.22E−14 187.5 52.1 91316 IGLL3P like polypeptide 1 /// /// ///immunoglobulin lambda- 91353 LOC91316 like polypeptide 3, 126208306_PM_x_at 3123 HLA- Major histocompatibility 1.25E−14 3695.8 2255.0DRB1 complex, class II, DR beta 1 127 217235_PM_x_at 28816 IGLV2-11immunoglobulin lambda 1.29E−14 196.8 37.8 variable 2-11 128209546_PM_s_at 8542 APOL1 apolipoprotein L, 1 1.33E−14 206.8 114.1 129203416_PM_at 963 CD53 CD53 molecule 1.34E−14 422.7 157.8 130211366_PM_x_at 834 CASP1 caspase 1, apoptosis-related 1.35E−14 186.087.1 cysteine peptidase (interleukin 1, beta, convertase) 131200797_PM_s_at 4170 MCL1 myeloid cell leukemia 1.38E−14 793.7 575.9sequence 1 (BCL2-related) 132 31845_PM_at 2000 ELF4 E74-like factor 4(ets 1.40E−14 60.7 34.3 domain transcription factor) 133 221841_PM_s_at9314 KLF4 Kruppel-like factor 4 (gut) 1.48E−14 132.3 65.2 134229391_PM_s_at 441168 FAM26F family with sequence 1.49E−14 212.1 73.7similarity 26, member F 135 203645_PM_s_at 9332 CD163 CD163 molecule1.51E−14 274.9 85.0 136 211643_PM_x_at 100510 IGK@ /// immunoglobulinkappa 1.61E−14 131.1 32.6 044 /// IGKC /// locus /// immunoglobulin28875 IGKV3D- kappa constant /// /// 3514 15 /// immunoglobulin kappa v/// LOC100510044 50802 137 205488_PM_at 3001 GZMA granzyme A (granzyme1, 1.82E−14 102.3 33.4 cytotoxic T-lymphocyte- associated serineesterase 3) 138 201464_PM_x_at 3725 JUN jun proto-oncogene 1.90E−14424.7 244.5 139 204774_PM_at 2123 EVI2A ecotropic viral integration1.95E−14 114.7 44.9 site 2 A 140 204336_PM_s_at 10287 RGS19 regulator ofG-protein 2.01E−14 135.7 67.0 signaling 19 141 244654_PM_at 64005 MYO1Gmyosin IG 2.03E−14 26.8 14.9 142 228442_PM_at 4773 NFATC2 nuclear factorof activated 2.06E−14 62.8 32.0 T-cells, cytoplasmic,calcineurin-dependent 2 143 206804_PM_at 917 CD3G CD3g molecule, gamma2.18E−14 36.4 17.3 (CD3-TCR complex) 144 201315_PM_x_at 10581 IFITM2interferon induced 2.21E−14 3303.7 2175.3 transmembrane protein 2 (1-8D) 145 203561_PM_at 2212 FCGR2A Fc fragment of IgG, low 2.22E−14 66.429.2 affinity IIa, receptor (CD32) 146 219117_PM_s_at 51303 FKBP11 FK506binding protein 11, 2.31E−14 341.3 192.9 19 kDa 147 242827_PM_x_at — — —2.37E−14 38.9 16.3 148 214768_PM_x_at 28299 IGK@ /// immunoglobulinkappa 2.38E−14 116.7 21.1 /// 3514 IGKC /// locus /// immunoglobulin ///IGKV1-5 kappa constant /// 50802 immunoglobulin kappa v 149 227253_PM_at1356 CP ceruloplasmin (ferroxidase) 2.49E−14 44.7 22.0 150 209619_PM_at972 CD74 CD74 molecule, major 2.51E−14 1502.3 864.9 histocompatibilitycomplex, class II invariant chain 151 208966_PM_x_at 3428 IFI16interferon, gamma-inducible 2.65E−14 644.9 312.6 protein 16 152239237_PM_at — — — 2.79E−14 25.3 14.5 153 213566_PM_at 6039 RNASE6ribonuclease, RNase A 2.82E−14 341.1 134.3 family, k6 154 201288_PM_at397 ARHGDIB Rho GDP dissociation 2.86E−14 542.2 308.1 inhibitor (GDI)beta 155 209606_PM_at 9595 CYTIP cytohesin 1 interacting 2.90E−14 79.032.9 protein 156 205758_PM_at 925 CD8A CD8a molecule 2.91E−14 60.3 22.2157 202953_PM_at 713 C1QB complement component 1, q 3.00E−14 401.1 142.5subcomponent, B chain 158 203233_PM_at 3566 IL4R interleukin 4 receptor3.06E−14 116.7 72.0 159 205270_PM_s_at 3937 LCP2 lymphocyte cytosolic3.12E−14 104.4 44.6 protein 2 (SH2 domain containing leukocyte proteinof 76 kDa) 160 223658_PM_at 9424 KCNK6 potassium channel, 3.18E−14 35.922.0 subfamily K, member 6 161 202637_PM_s_at 3383 ICAM1 intercellularadhesion 3.18E−14 89.1 45.7 molecule 1 162 202935_PM_s_at 6662 SOX9 SRY(sex determining 3.18E−14 117.0 46.1 region Y)-box 9 163 217986_PM_s_at11177 BAZ1A bromodomain adjacent to 3.21E−14 116.4 62.5 zinc fingerdomain, 1 A 164 210915_PM_x_at 28638 TRBC2 T cell receptor beta constant3.27E−14 129.7 37.5 2 165 223343_PM_at 58475 MS4A7 membrane-spanning 4-3.38E−14 346.0 128.3 domains, subfamily A, member 7 166 1552701_PM_a_at114769 CARD16 caspase recruitment domain 3.60E−14 273.3 119.8 family,member 16 167 226659_PM_at 50619 DEF6 differentially expressed in3.63E−14 35.2 22.2 FDCP 6 homolog (mouse) 168 213502_PM_x_at 91316LOC91316 glucuronidase, 3.63E−14 1214.7 419.3 beta/immunoglobulinlambda-like polypeptide 1 pseudogene 169 219332_PM_at 79778 MICALL2MICAL-like 2 3.71E−14 68.7 44.4 170 204891_PM_s_at 3932 LCKlymphocyte-specific protein 3.74E−14 43.4 17.8 tyrosine kinase 171224252_PM_s_at 53827 FXYD5 FXYD domain containing 3.76E−14 73.8 32.5 iontransport regulator 5 172 242878_PM_at — — — 3.90E−14 53.2 30.1 173224709_PM_s_at 56990 CDC42SE2 CDC42 small effector 2 4.07E−14 1266.2935.7 174 40420_PM_at 6793 STK10 serine/threonine kinase 10 4.32E−1442.0 24.4 175 218084_PM_x_at 53827 FXYD5 FXYD domain containing 4.52E−1489.2 39.1 ion transport regulator 5 176 218232_PM_at 712 C1QA complementcomponent 1, q 4.63E−14 197.0 85.8 subcomponent, A chain 177202208_PM_s_at 10123 ARL4C ADP-ribosylation factor- 4.63E−14 77.0 42.2like 4 C 178 220146_PM_at 51284 TLR7 toll-like receptor 7 4.93E−14 31.617.8 179 228752_PM_at 84766 EFCAB4B EF-hand calcium binding 5.05E−1420.6 12.1 domain 4 B 180 208948_PM_s_at 6780 STAU1 staufen, RNA binding5.23E−14 1766.0 2467.4 protein, homolog 1 (Drosophila) 181211645_PM_x_at — — — 5.24E−14 166.7 27.4 182 236295_PM_s_at 197358 NLRC3NLR family, CARD domain 5.28E−14 37.0 18.6 containing 3 183 224927_PM_at170954 KIAA1949 KIAA1949 5.44E−14 160.2 74.7 184 225258_PM_at 54751FBLIM1 filamin binding LIM 6.03E−14 228.7 125.4 protein 1 185202898_PM_at 9672 SDC3 syndecan 3 6.07E−14 64.8 32.0 186 218789_PM_s_at54494 C11orf71 chromosome 11 open 6.12E−14 175.8 280.8 reading frame 71187 204912_PM_at 3587 IL10RA interleukin 10 receptor, 6.25E−14 117.246.1 alpha 188 211582_PM_x_at 7940 LST1 leukocyte specific 6.48E−14121.2 49.4 transcript 1 189 214617_PM_at 5551 PRF1 perforin 1 (poreforming 6.77E−14 85.6 40.8 protein) 190 231887_PM_s_at 27143 KIAA1274KIAA1274 7.00E−14 45.6 30.0 191 223773_PM_s_at 85028 SNHG12 smallnucleolar RNA host 7.00E−14 174.8 93.2 gene 12 (non-protein coding) 192202644_PM_s_at 7128 TNFAIP3 tumor necrosis factor, alpha- 7.11E−14 278.2136.6 induced protein 3 193 211796_PM_s_at 28638 TRBC1 /// T cellreceptor beta constant 7.13E−14 250.2 63.6 /// TRBC2 1 /// T cellreceptor beta 28639 constant 2 194 206254_PM_at 1950 EGF epidermalgrowth factor 7.38E−14 176.6 551.3 195 216207_PM_x_at 28299 IGKC ///immunoglobulin kappa 7.51E−14 266.3 50.9 /// IGKV1-5 constant ///immunoglobulin 28904 /// kappa variable 1-5 /// /// 3514 IGKV1D-8immunoglobulin /// //// 652493 LOC652493 /// /// 652694 LOC652694 196232311_PM_at 567 B2M Beta-2-microglobulin 7.73E−14 83.7 35.2 197205466_PM_s_at 9957 HS3ST1 heparan sulfate 7.84E−14 96.0 42.1(glucosamine) 3-O- sulfotransferase 1 198 203332_PM_s_at 3635 INPP5Dinositol polyphosphate-5- 7.89E−14 42.6 24.0 phosphatase, 145 kDa 19964064_PM_at 55340 GIMAP5 GTPase, IMAP family 7.98E−14 170.6 111.4 member5 200 211644_PM_x_at 3514 /// IGK@ /// immunoglobulin kappa 8.04E−14246.9 47.5 50802 IGKC locus /// immunoglobulin kappa constant

TABLE 15 Biopsy Expression Profiling of Kidney Transplants: 2-WayClassifier CAN vs. TX (Brazilian Samples) Validation Cohort PostiveNegative Predictive Predictive Predictive Accuracy SensitivitySpecificity Value Value Algorithm Predictors Comparison AUC (%) (%) (%)(%) (%) Nearest 200 AR vs. TX 0.954 95 95 96 95 96 Centroid

Example 4

Expression Signatures to Distinguish Liver Transplant Injuries

Biomarker profiles diagnostic of specific types of graft injurypost-liver transplantation (LT), such as acute rejection (AR), hepatitisC virus recurrence (HCV-R), and other causes (acute dysfunction norejection/recurrence; ADNR) could enhance the diagnosis and managementof recipients. Our aim was to identify diagnostic genomic (mRNA)signatures of these clinical phenotypes in the peripheral blood andallograft tissue.

Patient Populations: The study population consisted of 114biopsy-documented Liver PAXgene whole blood samples comprised of 5different phenotypes: AR (n=25), ADNR (n=16), HCV(n=36), HCV+AR (n=13),and TX (n=24).

Gene Expression Profiling and Analysis: All samples were processed onthe Affymetrix HG-U133 PM only peg microarrays. To eliminate lowexpressed signals we used a signal filter cut-off that was datadependent, and therefore expression signals <Log 2 4.23 (median signalson all arrays) in all samples were eliminated leaving us with 48882probe sets from a total of 54721 probe sets. The first comparisonperformed was a 3-way ANOVA analysis of AR vs. ADNR vs. TX. This yielded263 differentially expressed probesets at a False Discovery rate (FDR<10%). We used these 263 probesets to build predictive models that coulddifferentiate the three classes. We used the Nearest Centroid (NC)algorithm to build the predictive models. We ran the predictive modelsusing two different methodologies and calculated the Area Under theCurve (AUC). First we did a one-level cross validation, where the datais first divided into 10 random partitions. At each iteration, 1/10 ofthe data is held out for testing while the remaining 9/10 of the data isused to fit the parameters of the model. This can be used to obtain anestimate of prediction accuracy for a single model. Then we modeled analgorithm for estimating the optimism, or over-fitting, in predictivemodels based on using bootstrapped datasets to repeatedly quantify thedegree of over-fitting in the model building process using sampling withreplacement. This optimism corrected AUC value is a nearly unbiasedestimate of the expected values of the optimism that would be obtainedin external validation (we used 1000 randomly created data sets). Table16a shows the optimism corrected AUCs for the 263 probesets that wereused to predict the accuracies for distinguishing between AR, ADNR andTX in Liver PAXgene samples. Table 16b shows the 263 probesets used fordistinguishing between AR, ADNR and TX in Liver PAXgene samples.

It is clear from the above Table 16a that the 263 probeset classifierwas able to distinguish the three phenotypes with very high predictiveaccuracy. The NC classifier had a sensitivity of 83%, specificity of93%, and positive predictive value of 95% and a negative predictivevalue of 78% for the AR vs. ADNR comparison. It is important to notethat these values did not change after the optimism correction where wesimulated 1000 data sets showing that these are really robustsignatures.

The next comparison we performed was a 3-way ANOVA of AR vs. HCV vs.HCV+AR which yielded 147 differentially expressed probesets at a p value<0.001. We chose to use this set of predictors because at an FDR <10% wehad only 18 predictors, which could possibly be due to the smallersample size of the HCV+AR (n=13) or a smaller set of differentiallyexpressed genes in one of the phenotypes. However, since this was adiscovery set to test the proof of principle whether there weresignatures that could distinguish samples that had an admixture of HCVand AR from the pure AR and the pure HCV populations, we ran thepredictive algorithms on the 147 predictors. Table 17a shows the AUCsfor the 147 probesets that were used to predict the accuracies fordistinguishing between AR, HCV and HCV+AR in Liver PAXgene samples.Table 17b shows the 147 probesets used for distinguishing between AR,HCV and HCV+AR in Liver PAXgene samples.

The NC classifier had a sensitivity of 87%, specificity of 97%, andpositive predictive value of 95% and a negative predictive value of 92%for the AR vs HCV comparison using the optimism correction where wesimulated 1000 data sets giving us confidence that the simulations thatwere done to mimic a real clinical situation did not alter therobustness of this set of predictors.

For the biopsies, again, we performed a 3-way ANOVA of AR vs. HCV vs.HCV+AR that yielded 320 differentially expressed probesets at an FDR<10%. We specifically did this because at a p-value <0.001 there wereover 950 probesets. We ran the predictive models on this set ofclassifiers in the same way mentioned for the PAXgene samples. Table 18ashows the AUCs for the one-level cross validation and the optimismcorrection for the classifier set comprised of 320 probesets that wereused to predict the accuracies for distinguishing between AR, HCV andHCV+AR in Liver biopsies. Table 18b shows the 320 probesets that usedfor distinguishing AR vs. HCV vs. HCV+AR in Liver biopsies.

In summary, for both the blood and the biopsy samples from livertransplant subjects we have classifier sets that can distinguish AR, HCVand HCV+AR with AUCs between 0.79-0.83 in blood and 0.69-0.83 in thebiopsies. We also have a signature from whole blood that can distinguishAR, ADNR and TX samples with AUC's ranging from 0.87-0.92.

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. Although any methods and materials similaror equivalent to those described herein can be used in the practice ortesting of the present invention, the preferred methods and materialsare described.

All publications, GenBank sequences, ATCC deposits, patents and patentapplications cited herein are hereby expressly incorporated by referencein their entirety and for all purposes as if each is individually sodenoted.

TABLE 16a AUCs for the 263 probes to predict AR, ADNR and TX in Liverwhole blood samples. Postive Negative Predictive Predictive PredictiveAccuracy Sensitivity Specificity Value Value Algorithm PredictorsComparison AUC (%) (%) (%) (%) (%) Nearest 263 AR vs. ADNR 0.882 88 8393 95 78 Centroid Nearest 263 AR vs. TX 0.943 95 95 95 95 95 CentroidNearest 263 ADNR vs. TX 0.883 88 93 83 78 95 Centroid

TABLE 16b The 263 probesets for distinguishing between AR, ADNR and TXin Liver PAXgene samples p-value ADNR - AR - TX - # Probeset ID GeneSymbol Gene Title (Phenotype) Mean Mean Mean 1 215415_PM_s_at LYSTlysosomal trafficking 3.79E−07 32.3 25.8 43.6 regulator 2 241038_PM_at —— 4.79E−07 16.1 21.0 16.4 3 230776_PM_at — — 2.10E−06 10.4 13.7 10.2 4212805_PM_at PRUNE2 prune homolog 2 4.09E−06 15.8 15.2 33.9 (Drosophila)5 215090_PM_x_at LOC440434 aminopeptidase puromycin 7.28E−06 164.6 141.0208.0 sensitive pseudogene 6 243625_PM_at — — 7.64E−06 31.2 20.8 29.9 7232222_PM_at C18orf49 chromosome 18 open 8.85E−06 33.7 35.7 42.4 readingframe 49 8 235341_PM_at DNAJC3 DnaJ (Hsp40) homolog, 1.06E−05 21.8 22.135.0 subfamily C, member 3 9 1557733_PM_a_at — — 1.21E−05 83.8 116.081.2 10 212906_PM_at GRAMD1B GRAM domain containing 1.26E−05 52.7 51.045.7 1B 11 1555874_PM_x_at MGC21881 hypothetical locus 1.53E−05 20.520.0 19.3 MGC21881 12 227645_PM_at PIK3R5 phosphoinositide-3-kinase,1.66E−05 948.4 824.5 1013.0 regulatory subunit 5 13 235744_PM_at PPTC7PTC7 protein phosphatase 1.73E−05 21.3 18.0 25.7 homolog (S. cerevisiae)14 1553873_PM_at KLHL34 kelch-like 34 (Drosophila) 1.89E−05 11.1 12.19.9 15 218408_PM_at TIMM10 translocase of inner 2.16E−05 125.9 137.799.4 mitochondrial membrane 10 homolog (yeast) 16 227486_PM_at NT5E5′-nucleotidase, ecto (CD73) 2.46E−05 14.7 18.6 15.6 17 231798_PM_at NOGnoggin 2.49E−05 17.0 25.9 15.1 18 205920_PM_at SLC6A6 solute carrierfamily 6 2.53E−05 25.9 25.0 39.3 (neurotransmitter transporter,taurine), member 6 19 222435_PM_s_at UBE2J1 ubiquitin-conjugating2.63E−05 212.6 292.4 324.0 enzyme E2, J1 (UBC6 homolog, yeast) 20207737_PM_at — — 2.89E−05 8.2 8.5 8.6 21 209644_PM_x_at CDKN2Acyclin-dependent kinase 2.91E−05 13.7 13.9 11.5 inhibitor 2A (melanoma,p16, inhibits CDK4) 22 241661_PM_at JMJD1C jumonji domain containing2.99E−05 18.4 21.9 34.8 1C 23 202086_PM_at MX1 myxovirus (influenzavirus) 3.04E−05 562.6 496.4 643.9 resistance 1, interferon- inducibleprotein p78 (mouse) 24 243819_PM_at — — 3.11E−05 766.7 495.1 661.8 25210524_PM_x_at — — 3.12E−05 154.5 209.2 138.6 26 217714_PM_x_at STMN1stathmin 1 3.39E−05 22.3 28.5 20.4 27 219659_PM_at ATP8A2 ATPase,aminophospholipid 3.65E−05 10.4 10.8 9.8 transporter, class I, type 8A,member 2 28 219915_PM_s_at SLC16A10 solute carrier family 16, 3.70E−0519.4 21.8 15.8 member 10 (aromatic amino acid transporter) 29214039_PM_s_at LAPTM4B lysosomal protein 3.81E−05 70.4 104.0 74.2transmembrane 4 beta 30 214107_PM_x_at LOC440434 aminopeptidasepuromycin 4.27E−05 182.8 155.0 224.7 sensitive pseudogene 31225408_PM_at MBP myelin basic protein 4.54E−05 34.1 32.6 47.9 321552623_PM_at HSH2D hematopoietic SH2 domain 4.93E−05 373.7 323.9 401.3containing 33 206974_PM_at CXCR6 chemokine (C-X-C motif) 5.33E−05 24.631.0 22.9 receptor 6 34 203764_PM_at DLGAP5 discs, large (Drosophila)5.41E−05 9.3 10.9 8.6 homolog-associated protein 5 35 213915_PM_at NKG7natural killer cell group 7 5.73E−05 2603.1 1807.7 1663.1 sequence 361570597_PM_at — — 5.86E−05 8.3 7.8 7.5 37 228290_PM_at PLK1S1 Polo-likekinase 1 substrate 6.00E−05 47.2 35.6 45.8 1 38 230753_PM_at PATL2protein associated with 6.11E−05 169.0 123.0 131.6 topoisomerase IIhomolog 2 (yeast) 39 202016_PM_at MEST mesoderm specific 6.25E−05 18.327.5 17.3 transcript homolog (mouse) 40 212730_PM_at SYNM synemin,intermediate 6.30E−05 16.7 19.5 14.4 filament protein 41 209203_PM_s_atBICD2 bicaudal D homolog 2 6.50E−05 197.8 177.0 256.6 (Drosophila) 421554397_PM_s_at UEVLD UEV and lactate/malate 6.59E−05 20.8 17.7 25.2dehyrogenase domains 43 217963_PM_s_at NGFRAP1 nerve growth factorreceptor 7.61E−05 505.9 713.1 555.7 (TNFRSF16) associated protein 1 44201656_PM_at ITGA6 integrin, alpha 6 7.75E−05 87.4 112.6 84.1 451553685_PM_s_at SP1 Sp1 transcription factor 7.83E−05 27.4 27.3 41.3 46236717_PM_at FAM179A family with sequence 8.00E−05 55.1 39.8 42.1similarity 179, member A 47 240913_PM_at FGFR2 fibroblast growth factor8.33E−05 9.2 9.6 10.2 receptor 2 48 243756_PM_at — — 8.47E−05 7.9 8.57.4 49 222036_PM_s_at MCM4 minichromosome 8.52E−05 29.5 35.1 25.4maintenance complex component 4 50 202644_PM_s_at TNFAIP3 tumor necrosisfactor, alpha- 8.57E−05 516.0 564.5 475.8 induced protein 3 51229625_PM_at GBP5 guanylate binding protein 5 9.23E−05 801.9 1014.7680.8 52 235545_PM_at DEPDC1 DEP domain containing 1 9.83E−05 8.0 8.78.3 53 204641_PM_at NEK2 NIMA (never in mitosis 0.000100269 10.2 12.510.0 gene a)-related kinase 2 54 213931_PM_at ID2 /// ID2B inhibitor ofDNA binding 2, 0.000101645 562.9 504.9 384.6 dominant negative helix-loop-helix protein /// inhibitor of 55 216125_PM_s_at RANBP9 RAN bindingprotein 9 0.000102366 35.4 37.0 50.3 56 205660_PM_at OASL2′-5′-oligoadenylate 0.000102776 470.5 394.6 493.4 synthetase-like 57222816_PM_s_at ZCCHC2 zinc finger, CCHC domain 0.000105861 301.3 308.7320.8 containing 2 58 1554696_PM_s_at TYMS thymidylate synthetase0.000110478 11.1 16.2 11.2 59 232229_PM_at SETX senataxin 0.00011307644.2 34.5 48.7 60 204929_PM_s_at VAMP5 vesicle-associated 0.000113182152.8 197.8 153.6 membrane protein 5 (myobrevin) 61 203819_PM_s_atIGF2BP3 insulin-like growth factor 2 0.000113349 45.4 75.4 51.1 mRNAbinding protein 3 62 210164_PM_at GZMB granzyme B (granzyme 2,0.000113466 955.2 749.5 797.1 cytotoxic T-lymphocyte- associated serineesterase 1) 63 202589_PM_at TYMS thymidylate synthetase 0.000113758 50.085.8 44.4 64 240507_PM_at — — 0.000116854 8.8 8.4 8.2 65 204475_PM_atMMP1 matrix metallopeptidase 1 0.000116902 9.2 15.4 9.6 (interstitialcollagenase) 66 222625_PM_s_at NDE1 nudE nuclear distribution0.000119388 60.6 55.3 72.2 gene E homolog 1 (A. nidulans) 671562697_PM_at LOC339988 hypothetical LOC339988 0.000125343 145.2 97.8105.4 68 218662_PM_s_at NCAPG non-SMC condensin I 0.000129807 11.5 14.810.7 complex, subunit G 69 201212_PM_at LGMN legumain 0.000129933 15.418.9 14.2 70 236191_PM_at — — 0.000133129 83.4 71.0 76.6 71 33736_PM_atSTOML1 stomatin (EPB72)-like 1 0.000137232 44.9 47.9 37.4 72221695_PM_s_at MAP3K2 mitogen-activated protein 0.000139287 76.4 76.8130.8 kinase kinase kinase 2 73 241692_PM_at — — 0.000142595 57.5 44.861.8 74 218741_PM_at CENPM centromere protein M 0.000142617 13.5 15.912.3 75 220684_PM_at TBX21 T-box 21 0.00014693 272.6 169.0 182.2 76233700_PM_at — — 0.000148072 125.7 74.1 156.3 77 217336_PM_at RPS10 ///ribosomal protein S10 /// 0.000149318 76.4 93.5 63.0 RPS10P7 ribosomalprotein S10 pseudogene 7 78 224391_PM_s_at SIAE sialic acidacetylesterase 0.000152602 28.8 42.0 33.8 79 201220_PM_x_at CTBP2C-terminal binding protein 2 0.000155512 1316.8 1225.6 1516.2 80204589_PM_at NUAK1 NUAK family, SNF1-like 0.00015593 13.1 10.1 9.6kinase, 1 81 1565254_PM_s_at ELL elongation factor RNA 0.000157726 29.224.5 40.4 polymerase II 82 243362_PM_s_at LOC641518 hypotheticalLOC641518 0.000159096 14.3 21.1 13.5 83 219288_PM_at C3orf14 chromosome3 open reading 0.000162164 31.1 43.4 28.0 frame 14 84 210797_PM_s_atOASL 2′-5′-oligoadenylate 0.000167239 268.3 219.6 304.2 synthetase-like85 243917_PM_at CLIC5 chloride intracellular 0.00017077 10.9 9.6 10.5channel 5 86 237538_PM_at — — 0.000176359 18.4 21.3 18.0 87 207926_PM_atGP5 glycoprotein V (platelet) 0.000178057 17.3 19.3 15.7 88 204103_PM_atCCL4 chemokine (C-C motif) 0.000178791 338.5 265.9 235.5 ligand 4 89212843_PM_at NCAM1 neural cell adhesion 0.000180762 28.7 25.8 33.5molecule 1 90 213629_PM_x_at MT1F metallothionein 1F 0.000186273 268.3348.4 234.3 91 212687_PM_at LIMS1 LIM and senescent cell 0.000188224859.6 1115.2 837.3 antigen-like domains 1 92 242898_PM_at EIF2AK2eukaryotic translation 0.000189906 82.5 66.4 81.2 initiation factor2-alpha kinase 2 93 208228_PM_s_at FGFR2 fibroblast growth factor0.000194281 8.9 11.1 8.7 receptor 2 94 219386_PM_s_at SLAMF8 SLAM familymember 8 0.000195762 18.6 23.0 16.5 95 201470_PM_at GSTO1 glutathioneS-transferase 0.000200503 1623.3 1902.3 1495.5 omega 1 96 204326_PM_x_atMT1X metallothionein 1X 0.000202494 370.5 471.8 313.0 97 213996_PM_atYPEL1 yippee-like 1 (Drosophila) 0.00020959 48.9 37.9 40.4 98203820_PM_s_at IGF2BP3 insulin-like growth factor 2 0.000210022 21.835.5 23.2 mRNA binding protein 3 99 218599_PM_at REC8 REC8 homolog(yeast) 0.000216761 42.6 43.3 41.1 100 216836_PM_s_at ERBB2 v-erb-b2erythroblastic 0.000217714 14.6 12.0 12.9 leukemia viral oncogenehomolog 2, neuro/glioblastoma derived o 101 213258_PM_at TFPI tissuefactor pathway 0.000218458 13.6 24.6 14.2 inhibitor (lipoprotein-associated coagulation inhibitor) 102 212859_PM_x_at MT1Emetallothionein 1E 0.000218994 166.9 238.1 134.5 103 214617_PM_at PRF1perforin 1 (pore forming 0.000222846 1169.2 822.3 896.0 protein) 10438918_PM_at SOX13 SRY (sex determining 0.000223958 14.1 10.9 11.8 regionY)-box 13 105 209969_PM_s_at STAT1 signal transducer and 0.000225341707.4 1874.3 1574.4 activator of transcription 1, 91 kDa 106205909_PM_at POLE2 polymerase (DNA directed), 0.000226803 14.0 16.0 12.7epsilon 2 (p59 subunit) 107 205612_PM_at MMRN1 multimerin 1 0.00022742510.3 15.5 11.1 108 218400_PM_at OAS3 2′-5′-oligoadenylate 0.000231476142.6 125.9 170.8 synthetase 3, 100 kDa 109 202503_PM_s_at KIAA0101KIAA0101 0.00023183 34.4 65.8 25.5 110 225636_PM_at STAT2 signaltransducer and 0.000234463 1425.0 1422.9 1335.1 activator oftranscription 2, 113 kDa 111 226579_PM_at — — 0.000234844 97.7 81.1104.6 112 1555764_PM_s_at TIMM10 translocase of inner 0.000235756 195.6204.3 158.7 mitochondrial membrane 10 homolog (yeast) 113 218429_PM_s_atC19orf66 chromosome 19 open 0.00024094 569.9 524.1 527.4 reading frame66 114 242155_PM_x_at RFFL ring finger and FYVE-like 0.000244391 62.846.7 72.0 domain containing 1 115 1556643_PM_at FAM125A Family withsequence 0.000244814 173.2 181.8 181.2 similarity 125, member A 116201957_PM_at PPP1R12B protein phosphatase 1, 0.000246874 93.3 63.9 107.9regulatory (inhibitor) subunit 12B 117 219716_PM_at APOL6 apolipoproteinL, 6 0.000248621 86.0 95.2 79.1 118 1554206_PM_at TMLHE trimethyllysinehydroxylase, 0.00026882 45.3 41.0 53.4 epsilon 119 207795_PM_s_at KLRD1killer cell lectin-like 0.000271145 294.6 201.8 192.5 receptor subfamilyD, member 1 120 210756_PM_s_at NOTCH2 notch 2 0.000271193 94.0 99.4142.6 121 219815_PM_at GAL3ST4 galactose-3-O- 0.00027183 17.3 19.9 16.4sulfotransferase 4 122 230405_PM_at C5orf56 chromosome 5 open reading0.000279441 569.5 563.2 521.9 frame 56 123 228617_PM_at XAF1 XIAPassociated factor 1 0.000279625 1098.8 1162.1 1043.0 124 240733_PM_at —— 0.000281133 87.3 54.9 81.2 125 209773_PM_s_at RRM2 ribonucleotidereductase M2 0.000281144 48.7 88.2 40.4 126 215236_PM_s_at PICALMphosphatidylinositol binding 0.000284863 61.6 65.8 113.8 clathrinassembly protein 127 229534_PM_at ACOT4 acyl-CoA thioesterase 40.000286097 17.1 13.2 12.6 128 215177_PM_s_at ITGA6 integrin, alpha 60.000287492 35.2 44.2 34.0 129 210321_PM_at GZMH granzyme H (cathepsinG- 0.000293732 1168.2 616.6 532.0 like 2, protein h-CCPX) 130206194_PM_at HOXC4 homeobox C4 0.000307767 20.0 17.1 15.1 131214115_PM_at VAMP5 Vesicle-associated 0.000308837 11.8 13.2 12.2membrane protein 5 (myobrevin) 132 211102_PM_s_at LILRA2 leukocyteimmunoglobulin- 0.000310388 94.3 78.0 129.0 like receptor, subfamily A(with TM domain), member 2 133 201818_PM_at LPCAT1lysophosphatidylcholine 0.000311597 662.1 517.3 651.3 acyltransferase 1134 53720_PM_at C19orf66 chromosome 19 open 0.000311821 358.7 323.7319.7 reading frame 66 135 221648_PM_s_at LOC100507192 hypothetical0.000312201 68.4 96.2 56.1 LOC100507192 136 236899_PM_at — — 0.0003183099.8 10.5 8.8 137 220467_PM_at — — 0.000319714 205.5 124.9 201.6 138218638_PM_s_at SPON2 spondin 2, extracellular 0.000320682 168.2 109.2137.0 matrix protein 139 211287_PM_x_at CSF2RA colony stimulating factor2 0.00032758 173.0 150.9 224.0 receptor, alpha, low-affinity(granulocyte-macrophage) 140 222058_PM_at — — 0.000332098 82.7 61.0101.6 141 224428_PM_s_at CDCA7 cell division cycle 0.000332781 22.9 31.519.6 associated 7 142 228675_PM_at LOC100131733 hypothetical 0.00034662715.2 17.6 14.5 LOC100131733 143 221248_PM_s_at WHSC1L1 Wolf-Hirschhornsyndrome 0.000354663 25.6 26.9 33.0 candidate 1-like 1 144 227697_PM_atSOCS3 suppressor of cytokine 0.000354764 103.6 192.4 128.8 signaling 3145 240661_PM_at LOC284475 hypothetical protein 0.000355764 79.3 53.989.5 LOC284475 146 204886_PM_at PLK4 polo-like kinase 4 0.000357085 8.911.8 8.9 147 216834_PM_at RGS1 regulator of G-protein 0.00035762 12.419.6 11.4 signaling 1 148 234089_PM_at — — 0.000359586 10.5 10.1 11.2149 236817_PM_at ADAT2 adenosine deaminase, 0.000362076 15.6 14.3 12.0tRNA-specific 2, TAD2 homolog (S. cerevisiae) 150 225349_PM_at ZNF496zinc finger protein 496 0.000363116 11.7 12.0 10.4 151 219863_PM_atHERC5 hect domain and RLD 5 0.000365254 621.1 630.8 687.7 152221985_PM_at KLHL24 kelch-like 24 (Drosophila) 0.000374117 183.6 184.7216.9 153 1552977_PM_a_at CNPY3 canopy 3 homolog 0.000378983 351.3 319.3381.7 (zebrafish) 154 1552667_PM_a_at SH2D3C SH2 domain containing 3C0.000380655 67.1 55.5 82.8 155 223502_PM_s_at TNFSF13B tumor necrosisfactor 0.000387301 2713.6 3366.3 2999.3 (ligand) superfamily, member 13b156 235139_PM_at GNGT2 guanine nucleotide binding 0.000389019 41.8 35.838.6 protein (G protein), gamma transducing activity polypeptide 157239979_PM_at — — 0.000389245 361.6 375.0 282.8 158 211882_PM_x_at FUT6fucosyltransferase 6 (alpha 0.000392613 11.1 11.6 10.6 (1,3)fucosyltransferase) 159 1562698_PM_x_at LOC339988 hypothetical LOC3399880.000394736 156.3 108.5 117.0 160 201890_PM_at RRM2 ribonucleotidereductase M2 0.000397796 23.6 42.5 21.7 161 243349_PM_at KIAA1324KIAA1324 0.000399335 15.4 12.8 20.2 162 243947_PM_s_at — — 0.0003998738.4 9.6 8.9 163 205483_PM_s_at ISG15 ISG15 ubiquitin-like 0.0004092821223.6 1139.6 1175.7 modifier 164 202705_PM_at CCNB2 cyclin B20.000409541 14.7 20.9 13.8 165 210835_PM_s_at CTBP2 C-terminal bindingprotein 2 0.000419387 992.3 926.1 1150.4 166 210554_PM_s_at CTBP2C-terminal binding protein 2 0.000429433 1296.5 1198.0 1519.5 167207085_PM_x_at CSF2RA colony stimulating factor 2 0.000439275 204.5190.0 290.3 receptor, alpha, low-affinity (granulocyte-macrophage) 168204205_PM_at APOBEC3G apolipoprotein B mRNA 0.000443208 1115.8 988.8941.4 editing enzyme, catalytic polypeptide-like 3G 169 227394_PM_atNCAM1 neural cell adhesion 0.000443447 19.1 19.4 25.3 molecule 1 1701568943_PM_at INPP5D inositol polyphosphate-5- 0.000450045 127.3 87.7114.0 phosphatase, 145 kDa 171 213932_PM_x_at HLA-A majorhistocompatibility 0.00045661 9270.0 9080.1 9711.9 complex, class I, A172 226202_PM_at ZNF398 zinc finger protein 398 0.000457538 84.5 78.498.3 173 233675_PM_s_at LOC374491 TPTE and PTEN 0.000457898 8.8 8.1 8.5homologous inositol lipid phosphatase pseudogene 174 220711_PM_at — —0.000458552 197.6 162.7 209.0 175 1552646_PM_at IL11RA interleukin 11receptor, 0.000463237 18.9 15.9 19.6 alpha 176 227055_PM_at METTL7Bmethyltransferase like 7B 0.000464226 11.1 15.0 11.8 177 223980_PM_s_atSP110 SP110 nuclear body protein 0.000471467 1330.9 1224.3 1367.3 178242367_PM_at — — 0.000471796 9.1 10.5 9.6 179 218543_PM_s_at PARP12 poly(ADP-ribose) 0.000476879 513.8 485.7 475.7 polymerase family, member 12180 204972_PM_at OAS2 2′-5′-oligoadenylate 0.000480934 228.5 215.8 218.7synthetase 2, 69/71 kDa 181 205746_PM_s_at ADAM17 ADAM metallopeptidase0.000480965 39.0 47.0 60.4 domain 17 182 1570645_PM_at — — 0.0004829489.3 9.1 8.4 183 211286_PM_x_at CSF2RA colony stimulating factor 20.000484313 261.3 244.7 345.6 receptor, alpha, low-affinity(granulocyte-macrophage) 184 1557545_PM_s_at RNF165 ring finger protein165 0.000489377 17.4 15.4 18.3 185 236545_PM_at — — 0.000491065 479.3367.8 526.2 186 228280_PM_at ZC3HAV1L zinc finger CCCH-type, 0.00049576825.3 36.4 23.7 antiviral 1-like 187 239798_PM_at — — 0.000505865 43.963.7 48.8 188 208055_PM_s_at HERC4 hect domain and RLD 4 0.00050728337.6 34.8 45.8 189 225692_PM_at CAMTA1 calmodulin binding 0.000515621244.8 308.6 245.1 transcription activator 1 190 210986_PM_s_at TPM1tropomyosin 1 (alpha) 0.000532739 344.0 379.1 391.9 191 205929_PM_atGPA33 glycoprotein A33 0.00053619 18.3 21.8 16.7 (transmembrane) 192242234_PM_at XAF1 XIAP associated factor 1 0.000537429 123.1 133.1 114.9193 206113_PM_s_at RAB5A RAB5A, member RAS 0.000543933 77.5 73.0 111.4oncogene family 194 242520_PM_s_at C1orf228 chromosome 1 open reading0.000547685 30.4 42.5 29.4 frame 228 195 229203_PM_at B4GALNT3beta-1,4-N-acetyl- 0.000549855 9.1 9.0 9.7 galactosaminyl transferase 3196 201601_PM_x_at IFITM1 interferon induced 0.000554665 6566.1 7035.77016.0 transmembrane protein 1 (9- 27) 197 221024_PM_s_at SLC2A10 solutecarrier family 2 0.000559418 8.3 9.7 8.6 (facilitated glucosetransporter), member 10 198 204439_PM_at IFI44L interferon-inducedprotein 0.000570113 343.5 312.4 337.1 44-like 199 215894_PM_at PTGDRprostaglandin D2 receptor 0.000571076 343.8 191.2 233.7 (DP) 200230846_PM_at AKAP5 A kinase (PRKA) anchor 0.000572655 10.7 10.9 9.6protein 5 201 210340_PM_s_at CSF2RA colony stimulating factor 20.000572912 154.2 146.3 200.8 receptor, alpha, low-affinity(granulocyte-macrophage) 202 237240_PM_at — — 0.000573343 9.4 10.7 9.4203 223836_PM_at FGFBP2 fibroblast growth factor 0.000574294 792.6 432.4438.4 binding protein 2 204 233743_PM_x_at S1PR5 sphingosine-1-phosphate0.000577598 9.3 8.6 9.6 receptor 5 205 229254_PM_at MFSD4 majorfacilitator superfamily 0.000581119 9.4 11.0 9.3 domain containing 4 206243674_PM_at LOC100240735 /// hypothetical 0.00058123 14.5 12.9 12.1LOC401522 LOC100240735 /// hypothetical LOC401522 207 208116_PM_s_atMAN1A1 mannosidase, alpha, class 0.000581644 34.4 39.1 55.0 1A, member 1208 222246_PM_at — — 0.000584363 15.9 13.9 17.9 209 212659_PM_s_at IL1RNinterleukin 1 receptor 0.000592065 87.2 94.5 116.3 antagonist 210204070_PM_at RARRES3 retinoic acid receptor 0.000597748 771.6 780.7613.7 responder (tazarotene induced) 3 211 219364_PM_at DHX58 DEXH(Asp-Glu-X-His) 0.000599299 92.7 85.2 85.3 box polypeptide 58 212204747_PM_at IFIT3 interferon-induced protein 0.000601375 603.1 576.7586.2 with tetratricopeptide repeats 3 213 240258_PM_at ENO1 enolase 1,(alpha) 0.000601726 9.0 9.3 10.5 214 210724_PM_at EMR3 egf-like modulecontaining, 0.000609884 622.3 437.3 795.3 mucin-like, hormonereceptor-like 3 215 204211_PM_x_at EIF2AK2 eukaryotic translation0.000611116 168.3 139.2 179.6 initiation factor 2-alpha kinase 2 216234975_PM_at GSPT1 G1 to S phase transition 1 0.000615027 16.6 16.3 21.4217 228145_PM_s_at ZNF398 zinc finger protein 398 0.000620533 373.0329.5 374.3 218 201565_PM_s_at ID2 inhibitor of DNA binding 2,0.000627734 1946.2 1798.1 1652.9 dominant negative helix- loop-helixprotein 219 226906_PM_s_at ARHGAP9 Rho GTPase activating 0.000630617636.2 516.2 741.5 protein 9 220 228412_PM_at LOC643072 hypotheticalLOC643072 0.00064178 213.5 186.6 282.7 221 233957_PM_at — — 0.00064427733.2 24.7 40.1 222 221277_PM_s_at PUS3 pseudouridylate synthase 30.000649375 86.6 99.3 77.8 223 203911_PM_at RAP1GAP RAP1 GTPaseactivating 0.000658389 106.6 40.1 116.1 protein 224 219352_PM_at HERC6hect domain and RLD 6 0.000659313 94.6 87.2 81.8 225 204994_PM_at MX2myxovirus (influenza virus) 0.000663904 1279.3 1147.0 1329.9 resistance2 (mouse) 226 227499_PM_at FZD3 frizzled homolog 3 0.00066528 11.7 11.09.8 (Drosophila) 227 222930_PM_s_at AGMAT agmatine ureohydrolase0.000665618 12.9 14.9 11.4 (agmatinase) 228 204575_PM_s_at MMP19 matrixmetallopeptidase 19 0.000668161 9.6 9.3 9.9 229 221038_PM_at — —0.000671518 8.7 8.2 9.3 230 233425_PM_at — — 0.000676591 76.4 70.6 77.9231 228972_PM_at LOC100306951 hypothetical 0.000679857 77.8 84.0 60.0LOC100306951 232 1560999_PM_a_at — — 0.000680202 9.8 10.6 10.7 233225931_PM_s_at RNF213 ring finger protein 213 0.000685818 339.7 313.2333.3 234 1559110_PM_at — — 0.000686358 11.7 11.5 13.4 235 207538_PM_atIL4 interleukin 4 0.000697306 8.3 9.5 8.7 236 210358_PM_x_at GATA2 GATAbinding protein 2 0.000702179 22.8 30.8 16.8 237 236341_PM_at CTLA4cytotoxic T-lymphocyte- 0.000706875 16.5 22.3 16.8 associated protein 4238 227416_PM_s_at ZCRB1 zinc finger CCHC-type and 0.000708438 388.0422.6 338.2 RNA binding motif 1 239 210788_PM_s_at DHRS7dehydrogenase/reductase 0.000719333 1649.6 1559.9 1912.3 (SDR family)member 7 240 213287_PM_s_at KRT10 keratin 10 0.000721676 557.8 585.1439.3 241 204026_PM_s_at ZWINT ZW10 interactor 0.000724993 23.3 31.119.9 242 239223_PM_s_at FBXL20 F-box and leucine-rich 0.00073241 106.875.0 115.9 repeat protein 20 243 234196_PM_at — — 0.000742539 140.6 81.3162.4 244 214931_PM_s_at SRPK2 SRSF protein kinase 2 0.00074767 30.030.9 45.3 245 216907_PM_x_at KIR3DL1 /// killer cell immunoglobulin-0.000748056 18.8 12.6 13.8 KIR3DL2 /// like receptor, three domains,LOC727787 long cytoplasmic tail, 1 /// k 246 243802_PM_at DNAH12 dynein,axonemal, heavy 0.000751054 8.8 9.9 8.4 chain 12 247 212070_PM_at GPR56G protein-coupled receptor 0.000760168 338.8 177.5 198.1 56 248239185_PM_at ABCA9 ATP-binding cassette, sub- 0.000767347 8.3 9.0 9.8family A (ABC1), member 9 249 229597_PM_s_at WDFY4 WDFY family member 40.000769378 128.9 96.6 148.4 250 216243_PM_s_at IL1RN interleukin 1receptor 0.000770819 131.4 134.1 180.7 antagonist 251 206991_PM_s_atCCR5 chemokine (C-C motif) 0.000771059 128.5 128.6 110.5 receptor 5 252219385_PM_at SLAMF8 SLAM family member 8 0.000789607 13.8 13.2 11.3 253240438_PM_at — — 0.000801737 10.8 10.4 11.4 254 226303_PM_at PGM5phosphoglucomutase 5 0.000802853 11.9 12.6 24.2 255 205875_PM_s_at TREX1three prime repair 0.000804871 254.9 251.6 237.6 exonuclease 1 2561566201_PM_at — — 0.000809569 10.4 9.0 10.2 257 211230_PM_s_at PIK3CDphosphoinositide-3-kinase, 0.000812288 20.4 20.3 24.6 catalytic, deltapolypeptide 258 202566_PM_s_at SVIL supervillin 0.000819718 43.9 41.067.5 259 244846_PM_at — — 0.000821386 75.0 55.1 84.9 260 208436_PM_s_atIRF7 interferon regulatory factor 0.000826426 264.0 262.4 281.2 7 261242020_PM_s_at ZBP1 Z-DNA binding protein 1 0.000828174 87.9 83.1 102.5262 203779_PM_s_at MPZL2 myelin protein zero-like 2 0.000830222 10.410.0 12.9 263 212458_PM_at SPRED2 sprouty-related, EVH1 0.000833211 11.511.4 13.4 domain containing 2

TABLE 17a AUCs for the 147 probes to predict AR, HCV and AR + HCV inLiver whole blood samples. Postive Negative Predictive PredictivePredictive Accuracy Sensitivity Specificity Value Value AlgorithmPredictors Comparison AUC (%) (%) (%) (%) (%) Nearest 147 AR vs. HCV0.952 96 87 97 95 92 Centroid Nearest 147 AR vs. HCV + AR 0.821 82 91 9295 85 Centroid Nearest 147 HCV vs. HCV + AR 0.944 94 92 97 92 97Centroid

TABLE 17b The 147 probesets for distinguishing between AR, HCV and HCV +AR in Liver PAXgene samples HCV + p-value AR - HCV - AR - # Probeset IDGene Symbol Gene Title (Phenotype) Mean Mean Mean 1 241038_PM_at — —4.76E−08 21.0 13.2 13.9 2 207737_PM_at — — 5.33E−06 8.5 8.4 10.2 31557733_PM_a_at — — 6.19E−06 116.0 50.8 64.5 4 228290_PM_at PLK1S1Polo-like kinase 1 substrate 1 7.97E−06 35.6 48.1 48.5 5 231798_PM_atNOG noggin 8.34E−06 25.9 12.6 9.4 6 214039_PM_s_at LAPTM4B lysosomalprotein 9.49E−06 104.0 58.3 68.5 transmembrane 4 beta 7 241692_PM_at — —9.61E−06 44.8 65.1 78.4 8 230776_PM_at — — 1.21E−05 13.7 10.4 9.5 9217963_PM_s_at NGFRAP1 nerve growth factor receptor 1.56E−05 713.1 461.2506.6 (TNFRSF16) associated protein 1 10 243917_PM_at CLIC5 chlorideintracellular channel 1.67E−05 9.6 10.9 11.6 5 11 219915_PM_s_atSLC16A10 solute carrier family 16, 1.77E−05 21.8 13.2 12.5 member 10(aromatic amino acid transporter) 12 1553873_PM_at KLHL34 kelch-like 34(Drosophila) 1.85E−05 12.1 9.6 9.1 13 227645_PM_at PIK3R5phosphoinositide-3-kinase, 2.12E−05 824.5 1003.6 1021.4 regulatorysubunit 5 14 1552623_PM_at HSH2D hematopoietic SH2 domain 2.54E−05 323.9497.5 445.4 containing 15 227486_PM_at NT5E 5′-nucleotidase, ecto (CD73)2.66E−05 18.6 13.4 12.2 16 219659_PM_at ATP8A2 ATPase, aminophospholipid4.00E−05 10.8 9.0 8.9 transporter, class I, type 8A, member 2 171555874_PM_x_at MGC21881 hypothetical locus 4.16E−05 20.0 21.0 31.4MGC21881 18 202086_PM_at MX1 myxovirus (influenza virus) 4.52E−05 496.41253.1 1074.1 resistance 1, interferon- inducible protein p78 (mouse) 19233675_PM_s_at LOC374491 TPTE and PTEN homologous 4.85E−05 8.1 8.2 9.9inositol lipid phosphatase pseudogene 20 219815_PM_at GAL3ST4galactose-3-O- 5.37E−05 19.9 17.0 14.3 sulfotransferase 4 21242898_PM_at EIF2AK2 eukaryotic translation 6.06E−05 66.4 116.6 108.7initiation factor 2-alpha kinase 2 22 215177_PM_s_at ITGA6 integrin,alpha 6 6.39E−05 44.2 26.9 23.9 23 236717_PM_at FAM179A family withsequence 6.43E−05 39.8 51.3 73.3 similarity 179, member A 24242520_PM_s_at C1orf228 chromosome 1 open reading 6.67E−05 42.5 29.126.4 frame 228 25 207926_PM_at GP5 glycoprotein V (platelet) 7.03E−0519.3 14.7 16.0 26 211882_PM_x_at FUT6 fucosyltransferase 6 (alpha8.11E−05 11.6 9.8 10.7 (1,3) fucosyltransferase) 27 201656_PM_at ITGA6integrin, alpha 6 8.91E−05 112.6 69.0 70.7 28 233743_PM_x_at S1PR5sphingosine-1-phosphate 9.26E−05 8.6 10.1 9.2 receptor 5 29210797_PM_s_at OASL 2′-5′-oligoadenylate 9.28E−05 219.6 497.2 446.0synthetase-like 30 243819_PM_at — — 9.55E−05 495.1 699.2 769.8 31209728_PM_at HLA-DRB4 /// major histocompatibility 0.000102206 33.8403.5 55.2 LOC100509582 complex, class II, DR beta 4 /// HLA class IIhistocompatibili 32 218638_PM_s_at SPON2 spondin 2, extracellular0.000103572 109.2 215.7 187.9 matrix protein 33 224293_PM_at TTTY10testis-specific transcript, Y- 0.000103782 8.7 11.1 10.2 linked 10(non-protein coding) 34 205660_PM_at OASL 2′-5′-oligoadenylate0.000105267 394.6 852.0 878.1 synthetase-like 35 230753_PM_at PATL2protein associated with 0.00010873 123.0 168.6 225.2 topoisomerase IIhomolog 2 (yeast) 36 243362_PM_s_at LOC641518 hypothetical LOC6415180.000114355 21.1 13.1 11.2 37 213996_PM_at YPEL1 yippee-like 1(Drosophila) 0.00012688 37.9 55.8 59.5 38 232222_PM_at C18orf49chromosome 18 open reading 0.000129064 35.7 65.1 53.0 frame 49 39205612_PM_at MMRN1 multimerin 1 0.000142028 15.5 9.9 11.2 40214791_PM_at SP140L SP140 nuclear body protein- 0.000150108 223.4 278.8285.8 like 41 240507_PM_at — — 0.000152167 8.4 9.5 8.1 42 203819_PM_s_atIGF2BP3 insulin-like growth factor 2 0.000174054 75.4 45.9 62.4 mRNAbinding protein 3 43 219288_PM_at C3orf14 chromosome 3 open reading0.000204911 43.4 29.2 51.0 frame 14 44 214376_PM_at — — 0.000213039 8.99.6 8.1 45 1568609_PM_s_at FAM91A2 /// family with sequence 0.000218802378.6 472.7 427.1 FLJ39739 /// similarity 91, member A2 /// LOC100286793/// hypothetical FLJ39739 /// LOC728855 /// hypothetica LOC728875 46207538_PM_at IL4 interleukin 4 0.000226354 9.5 8.3 8.9 47 243947_PM_s_at— — 0.000227289 9.6 8.4 8.6 48 204211_PM_x_at EIF2AK2 eukaryotictranslation 0.000227971 139.2 222.0 225.5 initiation factor 2-alphakinase 2 49 221648_PM_s_at LOC100507192 hypothetical LOC1005071920.000230544 96.2 62.4 62.1 50 202016_PM_at MEST mesoderm specifictranscript 0.000244181 27.5 17.0 19.3 homolog (mouse) 51 220684_PM_atTBX21 T-box 21 0.000260563 169.0 279.9 309.1 52 219018_PM_s_at CCDC85Ccoiled-coil domain containing 0.000261452 14.9 17.1 17.1 85C 53204575_PM_s_at MMP19 matrix metallopeptidase 19 0.00026222 9.3 9.3 11.354 1568943_PM_at INPP5D inositol polyphosphate-5- 0.000265939 87.7 143.4133.5 phosphatase, 145 kDa 55 220467_PM_at — — 0.000269919 124.9 215.2206.0 56 207324_PM_s_at DSC1 desmocollin 1 0.000280239 14.5 11.3 10.3 57218400_PM_at OAS3 2′-5′-oligoadenylate 0.000288454 125.9 316.7 299.6synthetase 3, 100 kDa 58 214617_PM_at PRF1 perforin 1 (pore forming0.000292417 822.3 1327.9 1415.4 protein) 59 239798_PM_at — — 0.00029426363.7 39.1 35.3 60 242020_PM_s_at ZBP1 Z-DNA binding protein 10.000303843 83.1 145.8 128.5 61 201786_PM_s_at ADAR adenosine deaminase,RNA- 0.000305042 2680.0 3340.9 3194.2 specific 62 234974_PM_at GALMgalactose mutarotase (aldose 0.000308107 63.1 88.8 93.7 1-epimerase) 63233121_PM_at — — 0.000308702 17.8 23.8 19.4 64 1557545_PM_s_at RNF165ring finger protein 165 0.000308992 15.4 24.2 22.1 65 229203_PM_atB4GALNT3 beta-1,4-N-acetyl- 0.000309508 9.0 10.1 8.6 galactosaminyltransferase 3 66 210164_PM_at GZMB granzyme B (granzyme 2, 0.000322925749.5 1241.7 1374.7 cytotoxic T-lymphocyte- associated serineesterase 1) 67 222468_PM_at KIAA0319L KIAA0319-like 0.000327428 286.7396.3 401.1 68 223272_PM_s_at C1orf57 chromosome 1 open reading0.000342477 69.0 54.6 77.4 frame 57 69 240913_PM_at FGFR2 fibroblastgrowth factor 0.00035107 9.6 10.6 11.7 receptor 2 70 230854_PM_at BCAR4breast cancer anti-estrogen 0.000352682 10.2 10.2 8.9 resistance 4 711562697_PM_at LOC339988 hypothetical LOC339988 0.000360155 97.8 151.3142.0 72 222732_PM_at TRIM39 tripartite motif-containing 39 0.000372812115.6 135.8 115.4 73 227917_PM_at FAM85A /// family with sequence0.000373226 206.8 154.1 154.9 FAM85B similarity 85, member A /// familywith sequence similarity 85, me 74 212687_PM_at LIMS1 LIM and senescentcell 0.000383722 1115.2 824.0 913.2 antigen-like domains 1 75216836_PM_s_at ERBB2 v-erb-b2 erythroblastic 0.000384613 12.0 16.3 14.3leukemia viral oncogene homolog 2, neuro/glioblastoma derived o 76236191_PM_at — — 0.000389259 71.0 95.0 114.3 77 213932_PM_x_at HLA-Amajor histocompatibility 0.000391535 9080.1 10344.2 10116.9 complex,class I, A 78 229254_PM_at MFSD4 major facilitator superfamily0.000393739 11.0 9.0 9.5 domain containing 4 79 212843_PM_at NCAM1neural cell adhesion molecule 0.000401596 25.8 50.2 37.7 1 80235256_PM_s_at GALM galactose mutarotase (aldose 0.000417617 58.0 79.890.2 1-epimerase) 81 1566201_PM_at — — 0.000420058 9.0 10.3 8.8 82204994_PM_at MX2 myxovirus (influenza virus) 0.000438751 1147.0 1669.11518.5 resistance 2 (mouse) 83 237240_PM_at — — 0.000440008 10.7 9.2 9.184 232478_PM_at — — 0.000447263 51.3 96.8 71.5 85 211410_PM_x_atKIR2DL5A killer cell immunoglobulin- 0.00045859 24.8 31.7 39.0 likereceptor, two domains, long cytoplasmic tail, 5A 86 1569551_PM_at — —0.00045899 12.7 17.5 17.9 87 222816_PM_s_at ZCCHC2 zinc finger, CCHCdomain 0.00046029 308.7 502.0 404.6 containing 2 88 1557071_PM_s_at NUB1negative regulator of 0.000481473 108.5 144.0 155.3 ubiquitin-likeproteins 1 89 219737_PM_s_at PCDH9 protocadherin 9 0.000485253 37.9 76.466.9 90 230563_PM_at RASGEF1A RasGEF domain family, 0.000488148 86.8121.7 139.4 member 1A 91 1560080_PM_at — — 0.000488309 9.9 11.0 12.2 92243756_PM_at — — 0.000488867 8.5 7.5 8.2 93 212730_PM_at SYNM synemin,intermediate 0.000521028 19.5 15.7 27.7 filament protein 941552977_PM_a_at CNPY3 canopy 3 homolog (zebrafish) 0.000521239 319.3395.2 261.4 95 218657_PM_at RAPGEFL1 Rap guanine nucleotide 0.00052996310.4 11.9 11.5 exchange factor (GEF)-like 1 96 228139_PM_at RIPK3receptor-interacting serine- 0.000530418 87.8 107.4 102.7 threoninekinase 3 97 38918_PM_at SOX13 SRY (sex determining region 0.00053473510.9 13.1 13.1 Y)-box 13 98 207795_PM_s_at KLRD1 killer cell lectin-likereceptor 0.000538523 201.8 309.8 336.1 subfamily D, member 1 99212906_PM_at GRAMD1B GRAM domain containing 1B 0.000540879 51.0 58.378.1 100 1561098_PM_at LOC641365 hypothetical LOC641365 0.000541122 8.78.5 10.1 101 209593_PM_s_at TOR1B torsin family 1, member B 0.000542383271.7 392.9 408.3 (torsin B) 102 223980_PM_s_at SP110 SP110 nuclear bodyprotein 0.000543351 1224.3 1606.9 1561.2 103 1554206_PM_at TMLHEtrimethyllysine hydroxylase, 0.000545869 41.0 50.6 46.5 epsilon 104240438_PM_at — — 0.000555441 10.4 12.0 13.1 105 212190_PM_at SERPINE2serpin peptidase inhibitor, 0.00055869 25.8 18.3 21.4 clade E (nexin,plasminogen activator inhibitor type 1), me 106 202081_PM_at IER2immediate early response 2 0.000568285 1831.1 2155.1 1935.4 107234089_PM_at — — 0.000585869 10.1 12.4 11.9 108 235139_PM_at GNGT2guanine nucleotide binding 0.000604705 35.8 50.6 51.5 protein (Gprotein), gamma transducing activity polypeptide 109 235545_PM_at DEPDC1DEP domain containing 1 0.00060962 8.7 8.4 10.0 110 242096_PM_at — —0.000618307 8.6 8.7 10.3 111 1553042_PM_a_at NFKBID nuclear factor ofkappa light 0.000619863 14.9 17.7 16.0 polypeptide gene enhancer inB-cells inhibitor, delta 112 209368_PM_at EPHX2 epoxide hydrolase 2,0.000625958 33.6 25.2 22.3 cytoplasmic 113 1553681_PM_a_at PRF1 perforin1 (pore forming 0.000629562 181.7 312.5 312.3 protein) 114 223836_PM_atFGFBP2 fibroblast growth factor 0.000647084 432.4 739.7 788.9 bindingprotein 2 115 210812_PM_at XRCC4 X-ray repair complementing 0.00067481113.2 15.5 16.5 defective repair in Chinese hamster cells 4 116230846_PM_at AKAP5 A kinase (PRKA) anchor 0.000678814 10.9 9.3 11.2protein 5 117 214567_PM_s_at XCL1 /// chemokine (C motif) ligand0.000680647 211.0 338.8 347.2 XCL2 1 /// chemokine (C motif) ligand 2118 237221_PM_at — — 0.00069712 9.9 8.7 9.5 119 232793_PM_at — —0.000698404 10.2 12.5 13.0 120 239479_PM_x_at — — 0.000700142 28.1 18.020.6 121 1558836_PM_at — — 0.000706412 33.2 53.1 45.7 1221562698_PM_x_at LOC339988 hypothetical LOC339988 0.000710123 108.5 165.5158.7 123 1552646_PM_at IL11RA interleukin 11 receptor, alpha0.000716149 15.9 19.4 16.3 124 236220_PM_at — — 0.000735209 9.9 8.3 7.7125 211379_PM_x_at B3GALNT1 beta-1,3-N- 0.00074606 8.9 8.2 9.7acetylgalactosaminyl- transferase 1 (globoside blood group) 126222830_PM_at GRHL1 grainyhead-like 1 0.000766774 14.7 10.5 10.4(Drosophila) 127 210948_PM_s_at LEF1 lymphoid enhancer-binding0.000768363 54.2 36.2 33.1 factor 1 128 244798_PM_at LOC100507492hypothetical LOC100507492 0.000800826 48.3 32.0 26.6 129 226666_PM_atDAAM1 dishevelled associated 0.000828238 64.3 50.3 47.8 activator ofmorphogenesis 1 130 229378_PM_at STOX1 storkhead box 1 0.000836722 10.28.5 9.6 131 206366_PM_x_at XCL1 chemokine (C motif) ligand 1 0.000839844194.1 306.8 324.9 132 214115_PM_at VAMP5 Vesicle-associated membrane0.000866755 13.2 12.1 16.6 protein 5 (myobrevin) 133 201212_PM_at LGMNlegumain 0.00087505 18.9 15.9 13.1 134 204863_PM_s_at IL6ST interleukin6 signal transducer 0.000897042 147.6 107.1 111.1 (gp130, oncostatin Mreceptor) 135 232229_PM_at SETX senataxin 0.000906105 34.5 45.3 36.9 1361555407_PM_s_at FGD3 FYVE, RhoGEF and PH 0.00091116 88.7 103.2 67.0domain containing 3 137 223127_PM_s_at C1orf21 chromosome 1 open reading0.000923068 9.1 10.3 11.0 frame 21 138 202458_PM_at PRSS23 protease,serine, 23 0.000924141 38.8 74.1 79.3 139 210606_PM_x_at KLRD1 killercell lectin-like receptor 0.000931313 289.8 421.9 473.0 subfamily D,member 1 140 212444_PM_at — — 0.000935909 10.2 11.6 10.2 141240893_PM_at — — 0.000940973 8.6 9.7 10.3 142 219474_PM_at C3orf52chromosome 3 open reading 0.000948853 8.9 10.0 10.2 frame 52 143235087_PM_at UNKL unkempt homolog 0.000967141 10.3 9.8 8.3(Drosophila)-like 144 216907_PM_x_at KIR3DL1 /// killer cellimmunoglobulin- 0.000987803 12.6 16.1 19.1 KIR3DL2 /// like receptor,three domains, LOC727787 long cytoplasmic tail, 1 /// k 145238402_PM_s_at FLJ35220 hypothetical protein 0.000990348 17.2 19.9 15.3FLJ35220 146 239273_PM_s_at MMP28 matrix metallopeptidase 28 0.00099380911.7 9.0 8.7 147 215894_PM_at PTGDR prostaglandin D2 receptor0.000994157 191.2 329.4 283.2 (DP)

TABLE 18a AUCs for the 320 probes to predict AR, ADNR and TX in Liverbiopsy samples. Postive Negative Predictive Predictive PredictiveAccuracy Sensitivity Specificity Value Value Algorithm PredictorsComparison AUC (%) (%) (%) (%) (%) Nearest 320 AR vs. HCV 0.937 94 84100 100 89 Centroid Nearest 320 AR vs. HCV + AR 1.000 100 100 100 100100 Centroid Nearest 320 HCV vs. HCV + AR 0.829 82 82 89 75 92 Centroid

TABLE 18b The 320 probesets that distinguish AR vs. HCV vs. HCV + AR inLiver Biopsies HCV + p-value AR - HCV - AR - # Probeset ID Gene SymbolGene Title (Phenotype) Mean Mean Mean 1 219863_PM_at HERC5 hect domainand RLD 5 1.53E−14 250.4 1254.7 1620.1 2 205660_PM_at OASL2′-5′-oligoadenylate 3.30E−14 128.1 1273.7 1760.9 synthetase-like 3210797_PM_s_at OASL 2′-5′-oligoadenylate 4.03E−14 62.0 719.3 915.2synthetase-like 4 214453_PM_s_at IFI44 interferon-induced protein 443.98E−13 342.2 1646.7 1979.2 5 218986_PM_s_at DDX60 DEAD(Asp-Glu-Ala-Asp) 5.09E−12 352.2 1253.2 1403.0 box polypeptide 60 6202869_PM_at OAS1 2′,5′-oligoadenylate synthetase 4.47E−11 508.0 1648.71582.5 1, 40/46 kDa 7 226702_PM_at CMPK2 cytidine monophosphate 5.23E−11257.3 1119.1 1522.6 (UMP-CMP) kinase 2, mitochondrial 8 203153_PM_atIFIT1 interferon-induced protein 5.31E−11 704.0 2803.7 3292.9 withtetratricopeptide repeats 1 9 202086_PM_at MX1 myxovirus (influenzavirus) 5.53E−11 272.4 1420.9 1836.8 resistance 1, interferon- inducibleprotein p78 (mouse) 10 242625_PM_at RSAD2 radical S-adenosyl methionine9.62E−11 56.2 389.2 478.2 domain containing 2 11 213797_PM_at RSAD2radical S-adenosyl methionine 1.43E−10 91.4 619.3 744.7 domaincontaining 2 12 204972_PM_at OAS2 2′-5′-oligoadenylate 2.07E−10 88.7402.1 536.1 synthetase 2, 69/71 kDa 13 219352_PM_at HERC6 hect domainand RLD 6 2.52E−10 49.5 206.7 272.8 14 205483_PM_s_at ISG15 ISG15ubiquitin-like modifier 3.68E−10 629.9 3181.1 4608.0 15 205552_PM_s_atOAS1 2′,5′-oligoadenylate synthetase 4.08E−10 224.7 868.7 921.2 1, 40/46kDa 16 204415_PM_at IFI6 interferon, alpha-inducible 5.83E−10 787.84291.7 5465.6 protein 6 17 205569_PM_at LAMP3 lysosomal-associated6.80E−10 21.8 91.3 126.2 membrane protein 3 18 219209_PM_at IFIH1interferon induced with 8.15E−10 562.3 1246.9 1352.7 helicase C domain 119 218400_PM_at OAS3 2′-5′-oligoadenylate 2.85E−09 87.9 265.2 364.5synthetase 3, 100 kDa 20 229450_PM_at IFIT3 interferon-induced protein4.69E−09 1236.3 2855.3 3291.7 with tetratricopeptide repeats 3 21226757_PM_at IFIT2 interferon-induced protein 5.35E−09 442.3 1083.21461.9 with tetratricopeptide repeats 2 22 204439_PM_at IFI44Linterferon-induced protein 44- 5.77E−09 146.3 794.4 1053.5 like 23227609_PM_at EPSTI1 epithelial stromal interaction 1 1.03E−08 396.91079.8 1370.3 (breast) 24 204747_PM_at IFIT3 interferon-induced protein1.59E−08 228.3 698.1 892.7 with tetratricopeptide repeats 3 25217502_PM_at IFIT2 interferon-induced protein 1.85E−08 222.9 575.1 745.9with tetratricopeptide repeats 2 26 228607_PM_at OAS22′-5′-oligoadenylate 2.16E−08 60.9 182.0 225.6 synthetase 2, 69/71 kDa27 224870_PM_at KIAA0114 KIAA0114 2.48E−08 156.5 81.8 66.0 28202411_PM_at IFI27 interferon, alpha-inducible 4.25E−08 1259.4 5620.85634.1 protein 27 29 223220_PM_s_at PARP9 poly (ADP-ribose) 4.48E−08561.7 1084.4 1143.1 polymerase family, member 9 30 208436_PM_s_at IRF7interferon regulatory factor 7 4.57E−08 58.9 102.9 126.9 31 219211_PM_atUSP18 ubiquitin specific peptidase 18 6.39E−08 51.0 183.6 196.1 32206133_PM_at XAF1 XIAP associated factor 1 7.00E−08 463.9 1129.2 1327.133 202446_PM_s_at PLSCR1 phospholipid scramblase 1 1.12E−07 737.8 1317.71419.8 34 235276_PM_at EPSTI1 epithelial stromal interaction 1 1.58E−0793.5 244.2 279.9 (breast) 35 219684_PM_at RTP4 receptor (chemosensory)1.64E−07 189.5 416.3 541.7 transporter protein 4 36 222986_PM_s_atSPHISA5 shisa homolog 5 (Xenopus 1.68E−07 415.0 586.9 681.4 laevis) 37223298_PM_s_at NT5C3 5′-nucleotidase, cytosolic III 2.06E−07 247.6 443.4474.7 38 228275_PM_at — — 2.24E−07 71.6 159.3 138.9 39 228617_PM_at XAF1XIAP associated factor 1 2.28E−07 678.3 1412.3 1728.5 40 214022_PM_s_atIFITM1 interferon induced 2.37E−07 1455.1 2809.3 3537.2 transmembraneprotein 1 (9- 27) 41 214059_PM_at IFI44 Interferon-induced protein 442.61E−07 37.1 158.8 182.5 42 206553_PM_at OAS2 2′-5′-oligoadenylate2.92E−07 18.9 45.6 53.1 synthetase 2, 69/71 kDa 43 214290_PM_s_atHIST2H2AA3 /// histone cluster 2, H2aa3 /// 3.50E−07 563.4 1151.2 1224.7HIST2H2AA4 histone cluster 2, H2aa4 44 1554079_PM_at GALNTL4UDP-N-acetyl-alpha-D- 3.58E−07 69.9 142.6 109.0 galactosamine:polypeptide N- acetylgalactosaminyltransferase- like 4 45 202430_PM_s_atPLSCR1 phospholipid scramblase 1 3.85E−07 665.7 1162.8 1214.5 46218280_PM_x_at HIST2H2AA3 /// histone cluster 2, H2aa3 /// 5.32E−07299.7 635.3 721.7 HIST2H2AA4 histone cluster 2, H2aa4 47 202708_PM_s_atHIST2H2BE histone cluster 2, H2be 7.04E−07 62.4 112.2 115.4 48222134_PM_at DDO D-aspartate oxidase 7.37E−07 76.0 134.9 118.4 49215071_PM_s_at HIST1H2AC histone cluster 1, H2ac 9.11E−07 502.4 1009.11019.0 50 209417_PM_s_at IFI35 interferon-induced protein 35 9.12E−07145.5 258.9 323.5 51 218543_PM_s_at PARP12 poly (ADP-ribose) 9.29E−07172.3 280.3 366.3 polymerase family, member 12 52 202864_PM_s_at SP100SP100 nuclear antigen 1.09E−06 372.5 604.2 651.9 53 217719_PM_at EIF3Leukaryotic translation 1.15E−06 4864.0 3779.0 3600.0 initiation factor3, subunit L 54 230314_PM_at — — 1.29E−06 36.0 62.5 59.5 55 202863_PM_atSP100 SP100 nuclear antigen 1.37E−06 500.0 751.3 815.8 56 236798_PM_at —— 1.38E−06 143.1 307.0 276.8 57 233555_PM_s_at SULF2 sulfatase 21.38E−06 47.0 133.4 119.0 58 236717_PM_at FAM179A family with sequence1.44E−06 16.5 16.1 24.2 similarity 179, member A 59 228531_PM_at SAMD9sterile alpha motif domain 1.54E−06 143.0 280.3 351.7 containing 9 60209911_PM_x_at HIST1H2BD histone cluster 1, H2bd 1.69E−06 543.7 999.91020.2 61 238039_PM_at LOC728769 hypothetical LOC728769 1.77E−06 62.895.5 97.2 62 222067_PM_x_at HIST1H2BD histone cluster 1, H2bd 1.78E−06378.1 651.6 661.4 63 201601_PM_x_at IFITM1 interferon induced 2.00E−061852.8 2956.0 3664.5 transmembrane protein 1 (9- 27) 64 213361_PM_atTDRD7 tudor domain containing 7 2.09E−06 158.5 314.1 328.6 65224998_PM_at CMTM4 CKLF-like MARVEL 2.15E−06 42.6 30.0 22.3transmembrane domain containing 4 66 222793_PM_at DDX58 DEAD(Asp-Glu-Ala-Asp) 2.41E−06 93.9 231.9 223.1 box polypeptide 58 67225076_PM_s_at ZNFX1 zinc finger, NFX1-type 2.55E−06 185.0 286.0 359.1containing 1 68 236381_PM_s_at WDR8 WD repeat domain 8 2.68E−06 41.661.5 64.8 69 202365_PM_at UNC119B unc-119 homolog B 2.72E−06 383.4 272.7241.0 (C. elegans) 70 215690_PM_x_at GPAA1 glycosylphosphatidylinositol2.75E−06 141.0 103.7 107.5 anchor attachment protein 1 homolog (yeast)71 211799_PM_x_at HLA-C major histocompatibility 2.77E−06 912.3 1446.01649.4 complex, class I, C 72 218943_PM_s_at DDX58 DEAD(Asp-Glu-Ala-Asp) 2.87E−06 153.9 310.7 350.7 box polypeptide 58 73235686_PM_at C2orf60 chromosome 2 open reading 3.32E−06 17.2 23.2 20.1frame 60 74 236193_PM_at LOC100506979 hypothetical LOC100506979 3.96E−0624.5 48.1 51.2 75 221767_PM_x_at HDLBP high density lipoprotein 4.00E−061690.9 1301.2 1248.4 binding protein 76 225796_PM_at PXK PX domaincontaining 4.08E−06 99.2 168.1 154.9 serine/threonine kinase 77209762_PM_x_at SP110 SP110 nuclear body protein 4.68E−06 150.5 242.3282.0 78 211060_PM_x_at GPAA1 glycosylphosphatidylinositol 4.74E−06153.1 113.3 116.8 anchor attachment protein 1 homolog (yeast) 79218019_PM_s_at PDXK pyridoxal (pyridoxine, 4.95E−06 304.5 210.8 198.6vitamin B6) kinase 80 219364_PM_at DHX58 DEXH (Asp-Glu-X-His) box5.46E−06 71.5 111.2 113.0 polypeptide 58 81 203281_PM_s_at UBA7ubiquitin-like modifier 6.79E−06 80.2 108.2 131.0 activating enzyme 7 82200923_PM_at LGALS3BP lectin, galactoside-binding, 6.99E−06 193.1 401.5427.4 soluble, 3 binding protein 83 208527_PM_x_at HIST1H2BE histonecluster 1, H2be 7.54E−06 307.7 529.7 495.4 84 219479_PM_at KDELC1 KDEL(Lys-Asp-Glu-Leu) 7.81E−06 74.1 131.5 110.6 containing 1 85 200950_PM_atARPC1A actin related protein 2/3 1.00E−05 1015.8 862.8 782.0 complex,subunit 1A, 41 kDa 86 213294_PM_at EIF2AK2 eukaryotic translation1.02E−05 390.4 690.7 651.6 initiation factor 2-alpha kinase 2 87205943_PM_at TDO2 tryptophan 2,3-dioxygenase 1.06E−05 7808.6 10534.710492.0 88 217969_PM_at C11orf2 chromosome 11 open reading 1.21E−05302.6 235.0 214.8 frame 2 89 1552370_PM_at C4orf33 chromosome 4 openreading 1.24E−05 58.4 124.5 97.2 frame 33 90 211911_PM_x_at HLA-B majorhistocompatibility 1.34E−05 4602.1 6756.7 7737.3 complex, class I, B 91232563_PM_at ZNF684 zinc finger protein 684 1.36E−05 131.9 236.2 231.892 203882_PM_at IRF9 interferon regulatory factor 9 1.43E−05 564.0 780.1892.0 93 225991_PM_at TMEM41A transmembrane protein 41A 1.45E−05 122.5202.1 179.6 94 239988_PM_at — — 1.53E−05 11.5 15.4 16.1 95 244434_PM_atGPR82 G protein-coupled receptor 82 1.55E−05 18.5 32.5 37.0 96201489_PM_at PPIF peptidylprolyl isomerase F 1.58E−05 541.7 899.5 672.997 221476_PM_s_at RPL15 ribosomal protein L15 1.58E−05 3438.3 2988.52742.8 98 244398_PM_x_at ZNF684 zinc finger protein 684 1.65E−05 57.296.9 108.5 99 208628_PM_s_at YBX1 Y box binding protein 1 1.66E−054555.5 3911.6 4365.0 100 211710_PM_x_at RPL4 ribosomal protein L41.73E−05 5893.1 4853.3 4955.4 101 229741_PM_at MAVS mitochondrialantiviral 1.78E−05 65.2 44.6 34.4 signaling protein 102 206386_PM_atSERPINA7 serpin peptidase inhibitor, 1.90E−05 3080.8 4251.6 4377.2 cladeA (alpha-1 antiproteinase, antitrypsin), member 7 103 213293_PM_s_atTRIM22 tripartite motif-containing 22 1.92E−05 1122.0 1829.2 2293.2 104200089_PM_s_at RPL4 ribosomal protein L4 1.93E−05 3387.5 2736.6 2823.9105 235037_PM_at TMEM41A transmembrane protein 41A 1.96E−05 134.7 218.5192.9 106 226459_PM_at PIK3AP1 phosphoinositide-3-kinase 2.10E−05 2152.42747.6 2929.7 adaptor protein 1 107 200023_PM_s_at EIF3F eukaryotictranslation 2.16E−05 1764.9 1467.2 1365.3 initiation factor 3, subunit F108 205161_PM_s_at PEX11A peroxisomal biogenesis factor 2.17E−05 51.987.3 76.9 11 alpha 109 225291_PM_at PNPT1 polyribonucleotide 2.18E−05287.0 469.1 455.0 nucleotidyltransferase 1 110 220445_PM_s_at CSAG2 ///CSAG family, member 2 /// 2.24E−05 16.3 91.2 120.9 CSAG3 CSAG family,member 3 111 226229_PM_s_at SSU72 SSU72 RNA polymerase II 2.24E−05 50.436.7 32.3 CTD phosphatase homolog (S. cerevisiae) 112 207418_PM_s_at DDOD-aspartate oxidase 2.48E−05 35.2 57.0 50.7 113 201786_PM_s_at ADARadenosine deaminase, RNA- 2.59E−05 1401.5 1867.9 1907.8 specific 114224724_PM_at SULF2 sulfatase 2 2.61E−05 303.6 540.1 553.9 115201618_PM_x_at GPAA1 glycosylphosphatidylinositol 2.63E−05 131.2 98.197.5 anchor attachment protein 1 homolog (yeast) 116 201154_PM_x_at RPL4ribosomal protein L4 2.78E−05 3580.5 2915.6 2996.2 117 200094_PM_s_atEEF2 eukaryotic translation 3.08E−05 3991.6 3248.5 3061.1 elongationfactor 2 118 208424_PM_s_at CIAPIN1 cytokine induced apoptosis 3.17E−0566.7 94.8 94.8 inhibitor 1 119 204102_PM_s_at EEF2 eukaryotictranslation 3.23E−05 3680.8 3102.7 2853.6 elongation factor 2 120203595_PM_s_at IFIT5 interferon-induced protein 3.44E−05 266.9 445.8450.9 with tetratricopeptide repeats 5 121 228152_PM_s_at DDX60L DEAD(Asp-Glu-Ala-Asp) 3.52E−05 136.1 280.8 304.5 box polypeptide 60-like 122201490_PM_s_at PPIF peptidylprolyl isomerase F 3.64E−05 209.2 443.5251.4 123 217933_PM_s_at LAP3 leucine aminopeptidase 3 3.81E−05 3145.63985.6 4629.9 124 203596_PM_s_at IFIT5 interferon-induced protein3.93E−05 195.9 315.8 339.0 with tetratricopeptide repeats 5 125220104_PM_at ZC3HAV1 zinc finger CCCH-type, 4.25E−05 23.3 53.1 57.7antiviral 1 126 213080_PM_x_at RPL5 ribosomal protein L5 4.28E−05 6986.76018.3 5938.6 127 208729_PM_x_at HLA-B major histocompatibility 4.58E−054720.9 6572.7 7534.4 complex, class I, B 128 32541_PM_at PPP3CC proteinphosphatase 3, 4.71E−05 63.3 79.7 81.3 catalytic subunit, gamma isozyme129 216231_PM_s_at B2M beta-2-microglobulin 4.79E−05 13087.7 14063.714511.1 130 206082_PM_at HCP5 HLA complex P5 4.91E−05 129.7 205.7 300.9131 213275_PM_x_at CTSB cathepsin B 4.93E−05 2626.4 2001.3 2331.0 132200643_PM_at HDLBP high density lipoprotein 5.04E−05 404.4 317.8 304.4binding protein 133 235309_PM_at RPS15A ribosomal protein S15a 5.08E−0598.5 77.4 55.3 134 209761_PM_s_at SP110 SP110 nuclear body protein5.33E−05 84.2 145.6 156.0 135 230753_PM_at PATL2 protein associated with5.55E−05 42.8 52.1 68.4 topoisomerase II homolog 2 (yeast) 136225369_PM_at ESAM endothelial cell adhesion 5.72E−05 14.9 13.1 11.9molecule 137 219255_PM_x_at IL17RB interleukin 17 receptor B 5.88E−05334.9 607.9 568.7 138 208392_PM_x_at SP110 SP110 nuclear body protein6.05E−05 60.2 96.1 115.5 139 221044_PM_s_at TRIM34 /// tripartitemotif-containing 6.07E−05 47.0 65.1 70.9 TRIM6- 34 /// TRIM6-TRIM34TRIM34 readthrough 140 1554375_PM_a_at NR1H4 nuclear receptor subfamily1, 6.23E−05 585.8 913.1 791.8 group H, member 4 141 210218_PM_s_at SP100SP100 nuclear antigen 6.41E−05 129.0 207.4 222.0 142 206340_PM_at NR1H4nuclear receptor subfamily 1, 6.67E−05 983.3 1344.6 1278.4 group H,member 4 143 222868_PM_s_at IL18BP interleukin 18 binding protein7.04E−05 72.0 45.4 90.9 144 204211_PM_x_at EIF2AK2 eukaryotictranslation 7.04E−05 144.8 215.9 229.8 initiation factor 2-alpha kinase2 145 231702_PM_at TDO2 Tryptophan 2,3-dioxygenase 7.09E−05 57.9 101.783.6 146 204906_PM_at RPS6KA2 ribosomal protein S6 kinase, 7.10E−05 40.128.3 28.7 90 kDa, polypeptide 2 147 218192_PM_at IP6K2 inositolhexakisphosphate 7.15E−05 84.0 112.5 112.7 kinase 2 148 211528_PM_x_atHLA-G major histocompatibility 7.45E−05 1608.7 2230.0 2613.2 complex,class I, G 149 208546_PM_x_at HIST1H2BB /// histone cluster 1, H2bb ///7.82E−05 65.3 131.7 112.0 HIST1H2BC /// histone cluster 1, H2bc ///HIST1H2BD /// histone cluster 1, H2bd /// his HIST1H2BE /// HIST1H2BG/// HIST1H2BH /// HIST1H2BI 150 204483_PM_at ENO3 enolase 3 (beta,muscle) 7.85E−05 547.8 1183.9 891.4 151 203148_PM_s_at TRIM14 tripartitemotif-containing 14 7.97E−05 590.8 803.6 862.4 152 1557120_PM_at EEF1A1Eukaryotic translation 8.14E−05 20.5 17.4 17.4 elongation factor 1 alpha1 153 203067_PM_at PDHX pyruvate dehydrogenase 8.21E−05 322.0 457.6413.2 complex, component X 154 224156_PM_x_at IL17RB interleukin 17receptor B 8.48E−05 426.4 755.4 699.9 155 203073_PM_at COG2 component ofoligomeric 9.64E−05 73.6 100.2 96.2 golgi complex 2 156 211937_PM_atEIF4B eukaryotic translation 9.68E−05 823.8 617.5 549.7 initiationfactor 4B 157 229804_PM_x_at CBWD2 COBW domain containing 2 9.69E−05170.0 225.0 229.1 158 225009_PM_at CMTM4 CKLF-like MARVEL 0.0001020754.0 40.5 32.3 transmembrane domain containing 4 159 221305_PM_s_atUGT1A8 /// UDP glucuronosyltransferase 0.000109701 214.8 526.8 346.9UGT1A9 1 family, polypeptide A8 /// UDP glucuronosyltransferase 1 1601557820_PM_at AFG3L2 AFG3 ATPase family gene 3- 0.000112458 1037.91315.0 1232.5 like 2 (S. cerevisiae) 161 237627_PM_at LOC100506318hypothetical LOC100506318 0.000115046 29.2 22.6 19.1 162 205819_PM_atMARCO macrophage receptor with 0.000115755 625.3 467.4 904.8 collagenousstructure 163 215313_PM_x_at HLA-A /// major histocompatibility0.000116881 6193.5 8266.5 9636.7 LOC100507703 complex, class I, A ///HLA class I histocompatibility antigen 164 226950_PM_at ACVRL1 activin Areceptor type II-like 0.000118584 28.2 25.1 35.5 1 165 213716_PM_s_atSECTM1 secreted and transmembrane 1 0.000118874 44.7 32.0 50.6 166207468_PM_s_at SFRP5 secreted frizzled-related 0.000121583 19.6 25.520.2 protein 5 167 218674_PM_at C5orf44 chromosome 5 open reading0.000124195 60.4 97.9 77.7 frame 44 168 219691_PM_at SAMD9 sterile alphamotif domain 0.000126093 29.6 49.5 53.9 containing 9 169 230795_PM_at —— 0.00012691 115.4 188.1 164.2 170 200941_PM_at HSBP1 heat shock factorbinding 0.000127149 559.2 643.2 623.6 protein 1 171 230174_PM_at LYPLAL1lysophospholipase-like 1 0.000127616 476.3 597.5 471.3 172214459_PM_x_at HLA-C major histocompatibility 0.000131095 4931.4 6208.36855.4 complex, class I, C 173 228971_PM_at LOC100505759 hypotheticalLOC100505759 0.000131603 210.7 139.7 91.6 174 217073_PM_x_at APOA1apolipoprotein A-I 0.000135801 12423.2 13707.0 13369.3 175 203964_PM_atNMI N-myc (and STAT) interactor 0.000138824 641.8 820.4 930.9 1761556988_PM_s_at CHD1L chromodomain helicase DNA 0.000142541 164.4 241.1226.9 binding protein 1-like 177 214890_PM_s_at FAM149A family withsequence 0.000144828 534.0 444.9 342.4 similarity 149, member A 178209115_PM_at UBA3 ubiquitin-like modifier 0.000144924 456.2 532.0 555.8activating enzyme 3 179 212284_PM_x_at TPT1 tumor protein,translationally- 0.000146465 15764.0 14965.0 14750.6 controlled 1 1801552274_PM_at PXK PX domain containing 0.000150376 24.9 37.1 43.1serine/threonine kinase 181 214889_PM_at FAM149A family with sequence0.00015075 295.1 236.6 152.6 similarity 149, member A 182 213287_PM_s_atKRT10 keratin 10 0.000151197 644.2 551.6 509.4 183 213051_PM_at ZC3HAV1zinc finger CCCH-type, 0.000152213 635.3 963.0 917.5 antiviral 1 184219731_PM_at CC2D2B Coiled-coil and C2 domain 0.000152224 37.5 50.5 50.5containing 2B 185 206211_PM_at SELE selectin E 0.000156449 76.0 35.122.8 186 217436_PM_x_at HLA-A /// major histocompatibility 0.000159936972.4 1408.3 1820.7 HLA-F /// complex, class I, A /// major HLA-Jhistocompatibility complex, clas 187 203970_PM_s_at PEX3 peroxisomalbiogenesis factor 0.000164079 387.4 540.4 434.7 3 188 1556643_PM_atFAM125A Family with sequence 0.000170998 68.0 107.1 95.8 similarity 125,member A 189 211529_PM_x_at HLA-G major histocompatibility 0.0001745592166.9 3107.2 3708.7 complex, class I, G 190 223187_PM_s_at ORMDL1ORM1-like 1 (S. cerevisiae) 0.000182187 784.3 918.4 945.5 1911566249_PM_at — — 0.000182326 15.1 12.7 12.3 192 218111_PM_s_at CMAScytidine monophosphate N- 0.000182338 242.6 418.6 310.9 acetylneuraminicacid synthetase 193 224361_PM_s_at IL17RB interleukin 17 receptor B0.000183121 231.0 460.8 431.4 194 217807_PM_s_at GLTSCR2 glioma tumorsuppressor 0.000185926 3262.6 2650.0 2523.4 candidate region gene 2 195222571_PM_at ST6GALNAC6 ST6 (alpha-N-acetyl- 0.00018814 31.7 24.2 25.0neuraminyl-2,3-beta- galactosyl-1,3)-N- acetylgalactosaminide alpha-2196 208012_PM_x_at SP110 SP110 nuclear body protein 0.000189717 245.7344.1 397.9 197 208579_PM_x_at H2BFS H2B histone family, member0.000192843 352.8 581.2 525.7 S 198 204309_PM_at CYP11A1 cytochromeP450, family 11, 0.000193276 17.5 27.3 29.2 subfamily A, polypeptide 1199 211956_PM_s_at EIF1 eukaryotic translation 0.000193297 6954.0 6412.96189.5 initiation factor 1 200 214455_PM_at HIST1H2BC histone cluster 1,H2bc 0.000196036 49.9 104.4 101.5 201 232140_PM_at — — 0.00019705 25.332.7 30.9 202 214054_PM_at DOK2 docking protein 2, 56 kDa 0.00019784328.6 25.1 39.9 203 210606_PM_x_at KLRD1 killer cell lectin-like receptor0.000201652 59.7 46.6 94.1 subfamily D, member 1 204 211943_PM_x_at TPT1tumor protein, translationally- 0.000202842 12849.6 11913.9 11804.6controlled 1 205 205506_PM_at VIL1 villin 1 0.000209043 67.1 28.6 21.7206 210514_PM_x_at HLA-G major histocompatibility 0.000214822 715.2976.4 1100.2 complex, class I, G 207 235885_PM_at P2RY12 purinergicreceptor P2Y, G- 0.000216727 21.1 30.2 49.1 protein coupled, 12 208212997_PM_s_at TLK2 tousled-like kinase 2 0.000217726 86.1 108.5 119.7209 211976_PM_at — — 0.000218277 145.9 115.9 104.8 210 231718_PM_at SLU7SLU7 splicing factor homolog 0.000221207 185.0 205.3 234.8 (S.cerevisiae) 211 225634_PM_at ZC3HAV1 zinc finger CCCH-type, 0.000224661388.3 511.6 490.5 antiviral 1 212 205936_PM_s_at HK3 hexokinase 3 (whitecell) 0.000231343 22.5 19.2 30.2 213 203912_PM_s_at DNASE1L1deoxyribonuclease I-like 1 0.000231815 171.2 151.3 183.8 214224603_PM_at — — 0.000232518 562.4 449.5 405.8 215 218085_PM_at CHMP5chromatin modifying protein 0.000232702 484.6 584.5 634.2 5 216204821_PM_at BTN3A3 butyrophilin, subfamily 3, 0.000235674 245.0 335.6401.3 member A3 217 217819_PM_at GOLGA7 golgin A7 0.000242192 845.31004.2 967.8 218 200629_PM_at WARS tryptophanyl-tRNA synthetase0.000244656 423.1 279.6 508.5 219 206342_PM_x_at IDS iduronate2-sulfatase 0.000246177 122.3 88.8 95.0 220 1560023_PM_x_at — —0.000247892 14.4 12.5 12.6 221 213706_PM_at GPD1 glycerol-3-phosphate0.000254153 124.3 227.8 162.9 dehydrogenase 1 (soluble) 222204312_PM_x_at CREB1 cAMP responsive element 0.000257352 28.9 41.8 34.8binding protein 1 223 230036_PM_at SAMD9L sterile alpha motif domain0.000265574 54.8 75.0 115.7 containing 9-like 224 222730_PM_s_at ZDHHC2zinc finger, DHHC-type 0.000270517 96.7 66.7 58.1 containing 2 225224225_PM_s_at ETV7 ets variant 7 0.000274744 32.8 55.4 71.0 2261294_PM_at UBA7 ubiquitin-like modifier 0.000290256 94.7 122.9 138.8activating enzyme 7 227 211075_PM_s_at CD47 CD47 molecule 0.000296663767.0 998.4 1061.6 228 228091_PM_at STX17 syntaxin 17 0.000298819 94.3134.9 110.7 229 205821_PM_at KLRK1 killer cell lectin-like receptor0.000299152 95.2 73.8 156.4 subfamily K, member 1 230 1563075_PM_s_at —— 0.000300425 41.4 63.6 82.2 231 224701_PM_at PARP14 poly (ADP-ribose)0.000301162 367.5 538.6 589.3 polymerase family, member 14 232209300_PM_s_at NECAP1 NECAP endocytosis 0.000304084 184.5 246.0 246.0associated 1 233 200937_PM_s_at RPL5 ribosomal protein L5 0.000308723893.3 3346.0 3136.1 234 208523_PM_x_at HIST1H2BI histone cluster 1,H2bi 0.000310294 79.8 114.5 115.8 235 210657_PM_s_at 4-Sep septin 40.000314978 122.1 78.4 61.6 236 239979_PM_at — — 0.000315949 40.3 78.8114.4 237 208941_PM_s_at SEPHS1 selenophosphate synthetase 1 0.000316337291.7 228.3 213.0 238 201649_PM_at UBE2L6 ubiquitin-conjugating enzyme0.000320318 928.3 1228.3 1623.0 E2L 6 239 211927_PM_x_at EEF1Geukaryotic translation 0.000325197 5122.7 4241.7 4215.5 elongationfactor 1 gamma 240 225458_PM_at LOC25845 hypothetical LOC258450.000337719 93.6 131.5 110.8 241 208490_PM_x_at HIST1H2BF histonecluster 1, H2bf 0.000339692 61.0 96.3 97.7 242 201322_PM_at ATP5B ATPsynthase, H+ 0.000342076 2068.5 2566.2 2543.7 transporting,mitochondrial F1 complex, beta polypeptide 243 221978_PM_at HLA-F majorhistocompatibility 0.00034635 49.8 69.5 100.6 complex, class I, F 244204031_PM_s_at PCBP2 poly(rC) binding protein 2 0.000351625 2377.62049.5 1911.5 245 243624_PM_at PIAS2 Protein inhibitor of activated0.000352892 17.7 15.4 14.1 STAT, 2 246 212998_PM_x_at HLA-DQB1 /// majorhistocompatibility 0.000359233 570.2 339.6 742.5 LOC100133583 complex,class II, DQ beta 1 /// HLA class II histocompatibili 247 204875_PM_s_atGMDS GDP-mannose 4,6- 0.00035965 73.9 41.2 45.5 dehydratase 248225721_PM_at SYNPO2 synaptopodin 2 0.000362084 69.1 43.3 32.1 249229696_PM_at FECH ferrochelatase 0.000362327 42.6 34.1 28.8 250208812_PM_x_at HLA-C major histocompatibility 0.000365707 7906.3 9602.610311.7 complex, class I, C 251 211666_PM_x_at RPL3 ribosomal protein L30.000376419 4594.1 4006.1 3490.3 252 219948_PM_x_at UGT2A3 UDPglucuronosyltransferase 0.000376972 219.5 454.5 350.3 2 family,polypeptide A3 253 204158_PM_s_at TCIRG1 T-cell, immune regulator 1,0.000384367 217.8 197.5 311.3 ATPase, H+ transporting, lysosomal V0subunit A3 254 209846_PM_s_at BTN3A2 butyrophilin, subfamily 3,0.000386605 424.5 612.5 703.0 member A2 255 243225_PM_at LOC283481hypothetical LOC283481 0.000388527 62.6 42.2 39.2 256 1554676_PM_at SRGNserglycin 0.000399135 11.6 12.7 15.0 257 202748_PM_at GBP2 guanylatebinding protein 2, 0.000406447 393.4 258.6 446.1 interferon-inducible258 238654_PM_at VSIG10L V-set and immunoglobulin 0.000411449 15.7 19.519.7 domain containing 10 like 259 218949_PM_s_at QRSL1 glutaminyl-tRNAsynthase 0.000413577 154.7 217.8 188.1 (glutamine-hydrolyzing)-like 1260 230306_PM_at VPS26B vacuolar protein sorting 26 0.000420436 80.866.4 59.0 homolog B (S. pombe) 261 204450_PM_x_at APOA1 apolipoproteinA-I 0.000427479 11811.2 13302.5 13014.4 262 213932_PM_x_at HLA-A majorhistocompatibility 0.000435087 7218.3 9083.8 10346.9 complex, class I, A263 201641_PM_at BST2 bone marrow stromal cell 0.000438494 217.2 396.5401.8 antigen 2 264 1552275_PM_s_at PXK PX domain containing 0.00043871824.7 38.6 34.4 serine/threonine kinase 265 210633_PM_x_at KRT10 keratin10 0.000438865 535.9 466.6 443.1 266 217874_PM_at SUCLG1 succinate-CoAligase, alpha 0.000441648 2582.3 3199.8 3034.6 subunit 267 223192_PM_atSLC25A28 solute carrier family 25, 0.000456748 157.1 178.0 220.5 member28 268 204820_PM_s_at BTN3A2 /// butyrophilin, subfamily 3, 0.0004573131264.5 1537.9 1932.9 BTN3A3 member A2 /// butyrophilin, subfamily 3,member A3 269 32069_PM_at N4BP1 NEDD4 binding protein 1 0.00045791 320.7400.4 402.0 270 208870_PM_x_at ATP5C1 ATP synthase, H+ 0.0004640123210.8 3791.7 3616.3 transporting, mitochondrial F1 complex, gammapolypeptide 1 271 207104_PM_x_at LILRB1 leukocyte immunoglobulin-0.000468733 52.9 52.0 80.6 like receptor, subfamily B (with TM and ITIMdomains), member 272 209035_PM_at MDK midkine (neurite growth-0.000469597 18.5 25.2 30.3 promoting factor 2) 273 230307_PM_atLOC100129794 similar to hCG1804255 0.000471715 17.3 14.8 13.5 274225255_PM_at MRPL35 mitochondrial ribosomal 0.000478299 44.4 59.0 49.3protein L35 275 229625_PM_at GBP5 guanylate binding protein 50.000478593 243.9 147.4 393.5 276 209140_PM_x_at HLA-B majorhistocompatibility 0.000478945 8305.0 10032.9 11493.8 complex, class I,B 277 210905_PM_x_at POU5F1P4 POU class 5 homeobox 1 0.000492713 11.913.7 13.9 pseudogene 4 278 218480_PM_at AGBL5 ATP/GTP bindingprotein-like 0.000494707 23.8 20.7 18.1 5 279 209253_PM_at SORBS3 sorbinand SH3 domain 0.000495796 97.5 86.2 78.2 containing 3 280207801_PM_s_at RNF10 ring finger protein 10 0.000508149 374.0 297.5327.3 281 212539_PM_at CHD1L chromodomain helicase DNA 0.000509089 482.2677.2 613.0 binding protein 1-like 282 224492_PM_s_at ZNF627 zinc fingerprotein 627 0.000513422 127.6 168.3 125.0 283 1557186_PM_s_at TPCN1 twopore segment channel 1 0.000513966 26.5 21.5 22.4 284 203610_PM_s_atTRIM38 tripartite motif-containing 38 0.000514783 100.5 139.2 156.0 285211530_PM_x_at HLA-G major histocompatibility 0.000525417 1034.7 1429.21621.6 complex, class I, G 286 201421_PM_s_at WDR77 WD repeat domain 770.000527341 114.5 143.9 133.4 287 200617_PM_at MLEC malectin 0.000529672244.8 174.2 147.7 288 1555982_PM_at ZFYVE16 zinc finger, FYVE domain0.000550743 27.5 35.4 27.8 containing 16 289 211345_PM_x_at EEF1Geukaryotic translation 0.000555581 4011.7 3333.0 3247.8 elongationfactor 1 gamma 290 1555202_PM_a_at RPRD1A regulation of nuclear pre-0.000561763 14.0 17.2 14.3 mRNA domain containing 1A 291 218304_PM_s_atOSBPL11 oxysterol binding protein-like 0.000565559 230.5 347.9 328.7 11292 219464_PM_at CA14 carbonic anhydrase XIV 0.000570778 64.9 43.5 32.6293 204278_PM_s_at EBAG9 estrogen receptor binding site 0.000570888482.5 591.0 510.6 associated, antigen, 9 294 218298_PM_s_at C14orf159chromosome 14 open reading 0.000571869 411.1 515.6 573.0 frame 159 295213675_PM_at — — 0.000576321 39.1 27.4 25.2 296 1555097_PM_a_at PTGFRprostaglandin F receptor (FP) 0.000581257 11.0 12.8 14.0 297209056_PM_s_at CDC5L CDC5 cell division cycle 5- 0.000582594 552.0 682.3659.9 like (S. pombe) 298 208912_PM_s_at CNP 2′,3′-cyclic nucleotide 3′0.00058579 308.8 415.8 392.9 phosphodiesterase 299 227018_PM_at DPP8dipeptidyl-peptidase 8 0.000587266 29.6 38.2 41.9 300 224650_PM_at MAL2mal, T-cell differentiation 0.000592979 600.4 812.5 665.3 protein 2 301217492_PM_s_at PTEN /// phosphatase and tensin 0.000601775 545.5 511.2426.0 PTENP1 homolog /// phosphatase and tensin homolog pseudogene 1 302211654_PM_x_at HLA-DQB1 major histocompatibility 0.000608592 538.8 350.2744.4 complex, class II, DQ beta 1 303 220312_PM_at FAM83E family withsequence 0.000609835 16.0 13.9 13.7 similarity 83, member E 304228230_PM_at PRIC285 peroxisomal proliferator- 0.00061118 42.0 55.4 57.6activated receptor A interacting complex 285 305 215171_PM_s_at TIMM17Atranslocase of inner 0.000624663 1432.1 1905.5 1715.4 mitochondrialmembrane 17 homolog A (yeast) 306 228912_PM_at VIL1 villin 1 0.00063054453.0 29.5 27.6 307 203047_PM_at STK10 serine/threonine kinase 100.000638877 41.0 39.1 54.7 308 232617_PM_at CTSS cathepsin S 0.0006409781192.9 1083.0 1561.2 309 236219_PM_at TMEM20 transmembrane protein 200.000648505 20.5 38.9 36.1 310 240681_PM_at — — 0.000649144 140.6 202.3192.8 311 1553317_PM_s_at GPR82 G protein-coupled receptor 820.000667359 13.3 20.1 21.2 312 212869_PM_x_at TPT1 tumor protein,translationally- 0.000669242 14240.7 13447.2 13475.2 controlled 1 313219356_PM_s_at CHMP5 chromatin modifying protein 0.000670413 1104.51310.4 1322.9 5 314 1552555_PM_at PRSS36 protease, serine, 360.000676354 14.2 12.9 11.8 315 203147_PM_s_at TRIM14 tripartitemotif-containing 14 0.000676359 334.8 419.3 475.4 316 43511_PM_s_at — —0.000678774 70.7 60.9 80.0 317 221821_PM_s_at C12orf41 chromosome 12open reading 0.000683679 180.0 213.8 206.9 frame 41 318 218909_PM_atRPS6KC1 ribosomal protein S6 kinase, 0.000686673 105.8 155.8 151.5 52kDa, polypeptide 1 319 232724_PM_at MS4A6A membrane-spanning 4-0.000686877 106.7 108.3 160.4 domains, subfamily A, member 6A 320218164_PM_at SPATA20 spermatogenesis associated 20 0.000693114 181.5130.4 156.0

1. A method of detecting gene expression products in a transplantrecipient on an immunosuppressive drug, the method comprising: (a)obtaining a blood sample, wherein the blood sample comprises mRNA fromthe transplant recipient on an immunosuppressive drug or DNA complementsof mRNA from the transplant recipient on an immunosuppressive drug; (b)performing a microarray assay or sequencing assay to determine anexpression level of the mRNA from the transplant recipient on animmunosuppressive drug or DNA complements of mRNA from the transplantrecipient on an immunosuppressive drug; (c) diagnosing, predicting, ormonitoring acute rejection in the transplant recipient by applying atrained algorithm to the expression level determined in step (b),wherein the trained algorithm comprises one or more classifiers, whereinthe trained algorithm is trained on 25 or more genes from Table 1a or1c, wherein the trained algorithm is capable of distinguishing betweenacute rejection and acute dysfunction with no rejection, and wherein thetrained algorithm has a negative predictive value of greater than 70%;and (d) administering a higher dosage of the immunosuppressive drug oradministering a new immunosuppressive drug in order to treat or preventthe acute rejection diagnosed, predicted, or monitored in the transplantrecipient in step (c).
 2. (canceled)
 3. A method of detecting or geneexpression products in a transplant recipient, the method comprising:(a) obtaining a blood sample, wherein the blood sample comprises mRNAfrom the transplant recipient or DNA complements of mRNA from thetransplant recipient; (b) performing a microarray assay or sequencingassay to determine an expression level mRNA from the transplantrecipient or DNA complements of mRNA from the transplant recipient; and(c) diagnosing, predicting, or monitoring acute rejection in thetransplant recipient by applying a trained algorithm to the expressionlevel determined in step (b), wherein the trained algorithm is trainedon 25 or more genes from Table 1a or 1c, wherein the trained algorithmis a three-way classifier capable of distinguishing between at leastthree conditions, and wherein one of the at least three conditions isacute rejection, wherein one of the at least three conditions is acutedysfunction with no rejection, and wherein the trained algorithm has anegative predictive value of greater than 70%; and (d) administering ahigher dosage of the immunosuppressive drug or administering a newimmunosuppressive drug in order to treat the acute rejection diagnosed,predicted, or monitored in the transplant recipient in step (c). 4-14.(canceled)
 15. (canceled)
 16. The method of claim 1, wherein the trainedalgorithm comprises a linear classifier.
 17. The method of claim 16,wherein the linear classifier comprises one or more linear discriminantanalysis, Fisher's linear discriminant, Naïve Bayes classifier, Logisticregression, Perceptron, Support vector machine (SVM) or a combinationthereof.
 18. The method of claim 1, wherein the trained algorithmcomprises a Diagonal Linear Discriminant Analysis (DLDA) algorithm. 19.The method of any one of claim 1, wherein the trained algorithmcomprises a Nearest Centroid algorithm.
 20. The method of any one ofclaim 1, wherein the trained algorithm comprises a Random Forestalgorithm or statistical bootstrapping.
 21. The method of any one ofclaim 1, wherein the trained algorithm comprises a Prediction Analysisof Microarrays (PAM) algorithm.
 22. The method of claim 1, wherein thetrained algorithm is not validated by a cohort-based analysis of anentire cohort.
 23. (canceled)
 24. The method of claim 1, wherein thedetermining the expression level in step (b) comprises determining theexpression level of 25 or more gene expression products with differentsequences.
 25. The method of claim 24, wherein the 25 or more geneexpression products correspond to less than 200 genes listed in Table 1aor 1c.
 26. (canceled)
 27. (canceled)
 28. (canceled)
 29. (canceled) 30.(canceled)
 31. (canceled) 32-34. (canceled)
 35. The method of claim 1,wherein the blood sample is a peripheral blood sample.
 36. The method ofclaim 1, wherein the blood sample is a whole blood sample. 37.(canceled)
 38. The method of claim 1, wherein the blood sample is notderived from tissue from a biopsy of a transplanted organ of thetransplant recipient on an immunosuppressive drug. 39-42. (canceled) 43.The method of claim 1, wherein the performing a microarray assay orsequencing assay in step (b) comprises performing an RNA sequencingassay on the mRNA from the transplant recipient on an immunosuppressivedrug.
 44. The method of claim 1, wherein the performing a microarrayassay or sequencing assay comprises performing a DNA sequencing assay onthe DNA complements of mRNA from the transplant recipient on animmunosuppressive drug.
 45. The method of claim 42, wherein the methodassay comprises performing a NextGen sequencing assay or massivelyparallel sequencing assay to determine the expression level of the mRNAfrom the transplant recipient on an immunosuppressive drug.
 46. Themethod of claim 1, wherein the determining the expression level in step(b) comprises determining the expression level of 25 or more geneslisted in Table 1a or 1c.
 47. (canceled)
 48. (canceled)
 49. The methodof claim 1, wherein the method has a sensitivity of at least about 80%.50. The method of claim 1, wherein the method has a specificity of atleast about 80%.
 51. (canceled)
 52. The method of claim 1, wherein thetransplant recipient on an immunosuppressive drug has a serum creatininelevel of at least 1.5 mg/dL.
 53. The method of claim 1, wherein thetransplant recipient on an immunosuppressive drug has a serum creatininelevel of at least 3 mg/dL.
 54. (canceled)
 55. The method of claim 1,wherein the transplant recipient is a kidney transplant recipient. 56.(canceled)
 57. (canceled)
 58. (canceled)
 59. The method of claim 3,wherein a frozen robust multichip average (fRMA) algorithm is used toproduce normalized expression level data in (b). 60-76. (canceled) 77.The method of claim 1, wherein the trained algorithm is further capableof distinguishing normal transplant functioning from acute rejection andfrom acute dysfunction with no rejection.
 78. The method of claim 1,wherein the method comprises administering an increased dose of theimmunosuppressive drug to the human subject in order to treat or preventthe acute rejection diagnosed, predicted or monitored in the transplantrecipient in step (c).
 79. The method of claim 1, wherein the methodcomprises administering a new immunosuppressive drug to the humansubject in order to treat or prevent the acute rejection diagnosed,predicted or monitored in the transplant recipient in step (c).
 80. Themethod of claim 1, wherein the immunosuppressive drug or newimmunosuppressive drug is selected from the group consisting of acalcineurin inhibitor, an mTOR inhibitor, an anti-proliferative, acorticosteroid, and an anti-T-cell antibody.
 81. The method of claim 1,wherein the immunosuppressive drug or new immunosuppressive drug isselected from the group consisting of cyclosporine, tacrolimus,azathioprine, mycophenolic acid, prednisolone, hydrocortisone,basiliximab, daclizumab, Orthoclone, anti-thymocyte globulin, andanti-lymphocyte globulin.
 82. The method of claim 1, wherein the trainedalgorithm is trained on 25 or more genes from Tables 1a and 1c.